[{"author":[{"last_name":"Kim","full_name":"Kim, Dongjin","first_name":"Dongjin"},{"id":"cbe3cda4-d82c-11eb-8dc7-8ff94289fcc9","full_name":"Cheng, Bingqing","orcid":"0000-0002-3584-9632","first_name":"Bingqing","last_name":"Cheng"}],"publication_status":"published","article_type":"original","year":"2026","oa":1,"article_processing_charge":"No","intvolume":"       164","main_file_link":[{"open_access":"1","url":"https://doi.org/10.48550/arXiv.2512.18029"}],"issue":"6","oa_version":"Preprint","month":"02","citation":{"chicago":"Kim, Dongjin, and Bingqing Cheng. “Long-Range Electrostatics for Machine Learning Interatomic Potentials Is Easier than We Thought.” <i>The Journal of Chemical Physics</i>. AIP Publishing, 2026. <a href=\"https://doi.org/10.1063/5.0316886\">https://doi.org/10.1063/5.0316886</a>.","apa":"Kim, D., &#38; Cheng, B. (2026). Long-range electrostatics for machine learning interatomic potentials is easier than we thought. <i>The Journal of Chemical Physics</i>. AIP Publishing. <a href=\"https://doi.org/10.1063/5.0316886\">https://doi.org/10.1063/5.0316886</a>","ieee":"D. Kim and B. Cheng, “Long-range electrostatics for machine learning interatomic potentials is easier than we thought,” <i>The Journal of Chemical Physics</i>, vol. 164, no. 6. AIP Publishing, 2026.","short":"D. Kim, B. Cheng, The Journal of Chemical Physics 164 (2026).","ama":"Kim D, Cheng B. Long-range electrostatics for machine learning interatomic potentials is easier than we thought. <i>The Journal of Chemical Physics</i>. 2026;164(6). doi:<a href=\"https://doi.org/10.1063/5.0316886\">10.1063/5.0316886</a>","mla":"Kim, Dongjin, and Bingqing Cheng. “Long-Range Electrostatics for Machine Learning Interatomic Potentials Is Easier than We Thought.” <i>The Journal of Chemical Physics</i>, vol. 164, no. 6, 060901, AIP Publishing, 2026, doi:<a href=\"https://doi.org/10.1063/5.0316886\">10.1063/5.0316886</a>.","ista":"Kim D, Cheng B. 2026. Long-range electrostatics for machine learning interatomic potentials is easier than we thought. The Journal of Chemical Physics. 164(6), 060901."},"corr_author":"1","publication":"The Journal of Chemical Physics","researchdata_availability":"yes","das_tickbox":"1","date_created":"2026-03-02T10:06:46Z","scopus_import":"1","department":[{"_id":"BiCh"}],"article_number":"060901","dataavailabilitystatement":"The RPBE-D3 bulk water dataset, training scripts, evaluation scripts, the trained CACE E + F + Qeq model, and CACE LES and MACE LES models used to produce results shown in Figs. 2(c)–2(e) are available at https://github.com/ChengUCB/les_fit.\r\n\r\nThe LES library is publicly available at https://github.com/ChengUCB/les. The CACE package with the LES implementation is available at https://github.com/BingqingCheng/cace. The MACE package with the LES implementation is available at https://github.com/ACEsuit/mace. The NequIP and Allegro LES extension package is available at https://github.com/ChengUCB/NequIP-LES. The MatGL package with the LES implementation is available at https://github.com/ChengUCB/matgl. The UMA package with the LES implementation is available at https://github.com/santi921/fairchem/tree/les_branch.","acknowledgement":"B.C. thanks Christoph Dellago for his mentorship and influence. In addition to his seminal contributions to statistical mechanics, Christoph Dellago is an early developer and adopter of machine learning interatomic potentials. B.C. did two exchanges in the groups of Christoph Dellago and Jörg Behler in 2018, with transformative impact on her research directions.\r\n\r\nWe thank Peichen Zhong and Daniel S. King for useful feedback on the manuscript and for the collaborations on the LES method.\r\n\r\nFunding acknowledgment: Research reported in this publication was supported by the National Institute Of General Medical Sciences of the National Institutes of Health under Award No. R35GM159986. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.","OA_place":"repository","arxiv":1,"quality_controlled":"1","OA_type":"free access","volume":164,"publication_identifier":{"eissn":["1089-7690"],"issn":["0021-9606"]},"language":[{"iso":"eng"}],"fulldoi":"https://doi.org/10.1063/5.0316886","external_id":{"arxiv":["2512.18029"]},"day":"14","date_published":"2026-02-14T00:00:00Z","supplementarymaterial":"no","date_updated":"2026-08-07T09:33:14Z","abstract":[{"text":"The lack of long-range electrostatics is a key limitation of modern machine learning interatomic potentials (MLIPs), hindering reliable applications to interfaces, charge-transfer reactions, polar and ionic materials, and biomolecules. In this Perspective, we distill two design principles behind the Latent Ewald Summation framework, which can capture long-range interactions, charges, and electrical response just by learning from standard energy and force training data: (i) use a Coulomb functional form with environment-dependent charges to capture electrostatic interactions, and (ii) avoid explicit training on ambiguous density functional theory partial charges. When both principles are satisfied, substantial flexibility remains: essentially any short-range MLIP can be augmented; charge equilibration schemes can be added when desired; dipoles and Born effective charges can be inferred or fine-tuned; and charge/spin-state embeddings or tensorial targets can be further incorporated. We also discuss current limitations and open challenges. Together, these minimal, physics-guided design rules suggest that incorporating long-range electrostatics into MLIPs is simpler and perhaps more broadly applicable than is commonly assumed.","lang":"eng"}],"title":"Long-range electrostatics for machine learning interatomic potentials is easier than we thought","doi":"10.1063/5.0316886","user_id":"317138e5-6ab7-11ef-aa6d-ffef3953e345","publisher":"AIP Publishing","_id":"21381","status":"public","type":"journal_article"},{"title":"Automatic feature selection and weighting in molecular systems using Differentiable Information Imbalance","abstract":[{"text":"Feature selection is essential in the analysis of molecular systems and many other fields, but several uncertainties remain: What is the optimal number of features for a simplified, interpretable model that retains essential information? How should features with different units be aligned, and how should their relative importance be weighted? Here, we introduce the Differentiable Information Imbalance (DII), an automated method to rank information content between sets of features. Using distances in a ground truth feature space, DII identifies a low-dimensional subset of features that best preserves these relationships. Each feature is scaled by a weight, which is optimized by minimizing the DII through gradient descent. This allows simultaneously performing unit alignment and relative importance scaling, while preserving interpretability. DII can also produce sparse solutions and determine the optimal size of the reduced feature space. We demonstrate the usefulness of this approach on two benchmark molecular problems: (1) identifying collective variables that describe conformations of a biomolecule, and (2) selecting features for training a machine-learning force field. These results show the potential of DII in addressing feature selection challenges and optimizing dimensionality in various applications. The method is available in the Python library DADApy.","lang":"eng"}],"file_date_updated":"2025-01-14T06:59:25Z","pmid":1,"license":"https://creativecommons.org/licenses/by-nc-nd/4.0/","date_updated":"2026-08-07T09:43:12Z","status":"public","_id":"18820","type":"journal_article","user_id":"317138e5-6ab7-11ef-aa6d-ffef3953e345","DOAJ_listed":"1","publisher":"Springer Nature","doi":"10.1038/s41467-024-55449-7","date_published":"2025-01-02T00:00:00Z","day":"02","external_id":{"pmid":["39747013"],"isi":["001389959100009"]},"language":[{"iso":"eng"}],"fulldoi":"https://doi.org/10.1038/s41467-024-55449-7","file":[{"file_id":"18846","access_level":"open_access","file_name":"2025_NatureComm_Wild.pdf","content_type":"application/pdf","date_updated":"2025-01-14T06:59:25Z","checksum":"b3d0f3568d9a87c494cf231a5324029a","file_size":1216738,"success":1,"creator":"dernst","relation":"main_file","date_created":"2025-01-14T06:59:25Z"}],"isi":1,"supplementarymaterial":"no","tmp":{"name":"Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)","short":"CC BY-NC-ND (4.0)","legal_code_url":"https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode","image":"/images/cc_by_nc_nd.png"},"das_tickbox":"1","date_created":"2025-01-12T23:04:00Z","department":[{"_id":"AnSa"},{"_id":"BiCh"}],"scopus_import":"1","publication":"Nature Communications","researchdata_availability":"yes","citation":{"ista":"Wild R, Wodaczek F, Del Tatto V, Cheng B, Laio A. 2025. Automatic feature selection and weighting in molecular systems using Differentiable Information Imbalance. Nature Communications. 16, 270.","mla":"Wild, Romina, et al. “Automatic Feature Selection and Weighting in Molecular Systems Using Differentiable Information Imbalance.” <i>Nature Communications</i>, vol. 16, 270, Springer Nature, 2025, doi:<a href=\"https://doi.org/10.1038/s41467-024-55449-7\">10.1038/s41467-024-55449-7</a>.","ama":"Wild R, Wodaczek F, Del Tatto V, Cheng B, Laio A. Automatic feature selection and weighting in molecular systems using Differentiable Information Imbalance. <i>Nature Communications</i>. 2025;16. doi:<a href=\"https://doi.org/10.1038/s41467-024-55449-7\">10.1038/s41467-024-55449-7</a>","short":"R. Wild, F. Wodaczek, V. Del Tatto, B. Cheng, A. Laio, Nature Communications 16 (2025).","ieee":"R. Wild, F. Wodaczek, V. Del Tatto, B. Cheng, and A. Laio, “Automatic feature selection and weighting in molecular systems using Differentiable Information Imbalance,” <i>Nature Communications</i>, vol. 16. Springer Nature, 2025.","apa":"Wild, R., Wodaczek, F., Del Tatto, V., Cheng, B., &#38; Laio, A. (2025). Automatic feature selection and weighting in molecular systems using Differentiable Information Imbalance. <i>Nature Communications</i>. Springer Nature. <a href=\"https://doi.org/10.1038/s41467-024-55449-7\">https://doi.org/10.1038/s41467-024-55449-7</a>","chicago":"Wild, Romina, Felix Wodaczek, Vittorio Del Tatto, Bingqing Cheng, and Alessandro Laio. “Automatic Feature Selection and Weighting in Molecular Systems Using Differentiable Information Imbalance.” <i>Nature Communications</i>. Springer Nature, 2025. <a href=\"https://doi.org/10.1038/s41467-024-55449-7\">https://doi.org/10.1038/s41467-024-55449-7</a>."},"has_accepted_license":"1","publication_identifier":{"eissn":["2041-1723"]},"volume":16,"quality_controlled":"1","OA_place":"publisher","OA_type":"gold","article_number":"270","acknowledgement":"The authors thank Dr. Matteo Carli for providing the CLN025 replica exchange MD trajectory and Matteo Allione for the fruitful discussions connected with the idea of the linear scaling estimator. This work was partially funded by NextGenerationEU through the Italian National Centre for HPC, Big Data, and Quantum Computing (Grant No. CN00000013 received by A.L.). A.L. also acknowledges financial support by the region Friuli Venezia Giulia (project F53C22001770002 received by A.L.).","dataavailabilitystatement":"The data generated by feature selection in this study have been deposited on OSF at the following URL: https://osf.io/swtg5. The processed molecular dynamics and H2O structure data are also available at OSF. The data files necessary for carrying out all analyses and source data are available at the same OSF URL. Source data are provided with this paper.","ddc":["570"],"author":[{"full_name":"Wild, Romina","first_name":"Romina","last_name":"Wild"},{"orcid":"0009-0000-1457-795X","full_name":"Wodaczek, Felix","first_name":"Felix","id":"8b4b6a9f-32b0-11ee-9fa8-bbe85e26258e","last_name":"Wodaczek"},{"last_name":"Del Tatto","full_name":"Del Tatto, Vittorio","first_name":"Vittorio"},{"last_name":"Cheng","first_name":"Bingqing","orcid":"0000-0002-3584-9632","full_name":"Cheng, Bingqing","id":"cbe3cda4-d82c-11eb-8dc7-8ff94289fcc9"},{"first_name":"Alessandro","full_name":"Laio, Alessandro","last_name":"Laio"}],"publication_status":"published","article_type":"original","month":"01","intvolume":"        16","oa_version":"Published Version","year":"2025","oa":1,"article_processing_charge":"Yes"},{"OA_place":"repository","quality_controlled":"1","arxiv":1,"OA_type":"green","publication_identifier":{"issn":["1549-9618"],"eissn":["1549-9626"]},"volume":21,"dataavailabilitystatement":"The training sets, training scripts, and trained potentials are available at https://github.com/ChengUCB/les_fit. The LES library is publicly available at https://github.com/ChengUCB/les. The CACE package with the LES implementation is available at https://github.com/BingqingCheng/cace. The MACE package with the LES implementation is available at https://github.com/ACEsuit/mace. The NequIP and Allegro LES extension package is available at https://github.com/ChengUCB/NequIP-LES. The MatGL package with the LES implementation is available at https://github.com/ChengUCB/matgl. The UMA package with the LES implementation is available at https://github.com/santi921/fairchem/tree/les_branch.","acknowledgement":"Research reported in this publication was supported by the National Institute Of General Medical Sciences of the National Institutes of Health under Award Number R35GM159986. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. D.K. and B.C. acknowledge funding from Toyota Research Institute Synthesis Advanced Research Challenge. T.J.I., D.S.K. and P.Z. acknowledge funding from BIDMaP Postdoctoral Fellowship. T.J.I. used resources of the National Energy Research Scientific Computing Center (NERSC), a Department of Energy Office of Science User Facility using NERSC award DOEERCAP0031751 ′GenAI@NERSC’. The authors thank Bowen Deng for valuable discussions on MatGL implementation, and thank Gabor Csanyi for stimulating discussions.","publication":"Journal of Chemical Theory and Computation","researchdata_availability":"no","date_created":"2026-01-04T23:01:33Z","das_tickbox":"1","department":[{"_id":"GradSch"},{"_id":"BiCh"}],"scopus_import":"1","citation":{"ista":"Kim D, Wang X, Vargas S, Zhong P, King DS, Inizan TJ, Cheng B. 2025. A universal augmentation framework for long-range electrostatics in machine learning interatomic potentials. Journal of Chemical Theory and Computation. 21(24), 12709–12724.","ama":"Kim D, Wang X, Vargas S, et al. A universal augmentation framework for long-range electrostatics in machine learning interatomic potentials. <i>Journal of Chemical Theory and Computation</i>. 2025;21(24):12709-12724. doi:<a href=\"https://doi.org/10.1021/acs.jctc.5c01400\">10.1021/acs.jctc.5c01400</a>","mla":"Kim, Dongjin, et al. “A Universal Augmentation Framework for Long-Range Electrostatics in Machine Learning Interatomic Potentials.” <i>Journal of Chemical Theory and Computation</i>, vol. 21, no. 24, American Chemical Society, 2025, pp. 12709–24, doi:<a href=\"https://doi.org/10.1021/acs.jctc.5c01400\">10.1021/acs.jctc.5c01400</a>.","apa":"Kim, D., Wang, X., Vargas, S., Zhong, P., King, D. S., Inizan, T. J., &#38; Cheng, B. (2025). A universal augmentation framework for long-range electrostatics in machine learning interatomic potentials. <i>Journal of Chemical Theory and Computation</i>. American Chemical Society. <a href=\"https://doi.org/10.1021/acs.jctc.5c01400\">https://doi.org/10.1021/acs.jctc.5c01400</a>","ieee":"D. Kim <i>et al.</i>, “A universal augmentation framework for long-range electrostatics in machine learning interatomic potentials,” <i>Journal of Chemical Theory and Computation</i>, vol. 21, no. 24. American Chemical Society, pp. 12709–12724, 2025.","short":"D. Kim, X. Wang, S. Vargas, P. Zhong, D.S. King, T.J. Inizan, B. Cheng, Journal of Chemical Theory and Computation 21 (2025) 12709–12724.","chicago":"Kim, Dongjin, Xiaoyu Wang, Santiago Vargas, Peichen Zhong, Daniel S. King, Theo Jaffrelot Inizan, and Bingqing Cheng. “A Universal Augmentation Framework for Long-Range Electrostatics in Machine Learning Interatomic Potentials.” <i>Journal of Chemical Theory and Computation</i>. American Chemical Society, 2025. <a href=\"https://doi.org/10.1021/acs.jctc.5c01400\">https://doi.org/10.1021/acs.jctc.5c01400</a>."},"corr_author":"1","main_file_link":[{"open_access":"1","url":"https://doi.org/10.48550/arXiv.2507.14302"}],"issue":"24","intvolume":"        21","oa_version":"Preprint","month":"12","oa":1,"year":"2025","article_processing_charge":"No","author":[{"full_name":"Kim, Dongjin","first_name":"Dongjin","last_name":"Kim"},{"id":"8dff9c62-32b0-11ee-9fa8-fc73025e10f3","full_name":"Wang, Xiaoyu","first_name":"Xiaoyu","last_name":"Wang"},{"last_name":"Vargas","first_name":"Santiago","full_name":"Vargas, Santiago"},{"full_name":"Zhong, Peichen","first_name":"Peichen","last_name":"Zhong"},{"full_name":"King, Daniel S.","first_name":"Daniel S.","last_name":"King"},{"first_name":"Theo Jaffrelot","full_name":"Inizan, Theo Jaffrelot","last_name":"Inizan"},{"last_name":"Cheng","id":"cbe3cda4-d82c-11eb-8dc7-8ff94289fcc9","first_name":"Bingqing","full_name":"Cheng, Bingqing","orcid":"0000-0002-3584-9632"}],"publication_status":"published","article_type":"original","status":"public","_id":"20926","type":"journal_article","doi":"10.1021/acs.jctc.5c01400","user_id":"317138e5-6ab7-11ef-aa6d-ffef3953e345","publisher":"American Chemical Society","abstract":[{"lang":"eng","text":"Most current machine learning interatomic potentials (MLIPs) rely on short-range approximations, without explicit treatment of long-range electrostatics. To address this, we recently developed the Latent Ewald Summation (LES) method, which infers electrostatic interactions, polarization, and Born effective charges (BECs), just by learning from energy and force training data. Here, we present LES as a standalone library, compatible with any short-range MLIP, and demonstrate its integration with methods such as MACE, NequIP, Allegro, CACE, CHGNet, and UMA. We benchmark LES-enhanced models on distinct systems, including bulk water, polar dipeptides, and gold dimer adsorption on defective substrates, and show that LES not only captures correct electrostatics but also improves accuracy. Additionally, we scale LES to large and chemically diverse data by training MACELES-OFF on the SPICE set containing molecules and clusters, making a universal MLIP with electrostatics for organic systems, including biomolecules. MACELES-OFF is more accurate than its short-range counterpart (MACE-OFF) trained on the same data set, predicts dipoles and BECs reliably, and has better descriptions of bulk liquids. By enabling efficient long-range electrostatics without directly training on electrical properties, LES paves the way for electrostatic foundation MLIPs."}],"title":"A universal augmentation framework for long-range electrostatics in machine learning interatomic potentials","page":"12709-12724","date_updated":"2026-08-07T09:35:25Z","pmid":1,"supplementarymaterial":"no","day":"10","date_published":"2025-12-10T00:00:00Z","language":[{"iso":"eng"}],"fulldoi":"https://doi.org/10.1021/acs.jctc.5c01400","external_id":{"arxiv":["2507.14302"],"pmid":["41368735 "]}},{"author":[{"full_name":"Zhong, Peichen","first_name":"Peichen","last_name":"Zhong"},{"last_name":"Kim","full_name":"Kim, Dongjin","first_name":"Dongjin"},{"last_name":"King","full_name":"King, Daniel S.","first_name":"Daniel S."},{"last_name":"Cheng","full_name":"Cheng, Bingqing","orcid":"0000-0002-3584-9632","first_name":"Bingqing","id":"cbe3cda4-d82c-11eb-8dc7-8ff94289fcc9"}],"article_type":"original","publication_status":"published","ddc":["540"],"oa":1,"year":"2025","article_processing_charge":"Yes","month":"12","oa_version":"Published Version","intvolume":"        11","citation":{"ista":"Zhong P, Kim D, King DS, Cheng B. 2025. Machine learning interatomic potential can infer electrical response. npj Computational Materials. 11, 384.","ama":"Zhong P, Kim D, King DS, Cheng B. Machine learning interatomic potential can infer electrical response. <i>npj Computational Materials</i>. 2025;11. doi:<a href=\"https://doi.org/10.1038/s41524-025-01911-z\">10.1038/s41524-025-01911-z</a>","mla":"Zhong, Peichen, et al. “Machine Learning Interatomic Potential Can Infer Electrical Response.” <i>Npj Computational Materials</i>, vol. 11, 384, Springer Nature, 2025, doi:<a href=\"https://doi.org/10.1038/s41524-025-01911-z\">10.1038/s41524-025-01911-z</a>.","ieee":"P. Zhong, D. Kim, D. S. King, and B. Cheng, “Machine learning interatomic potential can infer electrical response,” <i>npj Computational Materials</i>, vol. 11. Springer Nature, 2025.","apa":"Zhong, P., Kim, D., King, D. S., &#38; Cheng, B. (2025). Machine learning interatomic potential can infer electrical response. <i>Npj Computational Materials</i>. Springer Nature. <a href=\"https://doi.org/10.1038/s41524-025-01911-z\">https://doi.org/10.1038/s41524-025-01911-z</a>","short":"P. Zhong, D. Kim, D.S. King, B. Cheng, Npj Computational Materials 11 (2025).","chicago":"Zhong, Peichen, Dongjin Kim, Daniel S. King, and Bingqing Cheng. “Machine Learning Interatomic Potential Can Infer Electrical Response.” <i>Npj Computational Materials</i>. Springer Nature, 2025. <a href=\"https://doi.org/10.1038/s41524-025-01911-z\">https://doi.org/10.1038/s41524-025-01911-z</a>."},"corr_author":"1","has_accepted_license":"1","das_tickbox":"1","date_created":"2026-01-15T12:17:07Z","department":[{"_id":"BiCh"}],"scopus_import":"1","publication":"npj Computational Materials","researchdata_availability":"yes","article_number":"384","dataavailabilitystatement":"The training sets, training scripts, BEC inference scripts, and trained CACE potentials are available at https://github.com/BingqingCheng/LES-BEC.","acknowledgement":"The authors thank for valuable discussions with Pinchen Xie, David Limmer, Jeff Neaton, and Greg Voth. The authors thank Sebastien Hamel for providing the DFT MD trajectories for superionic water, and help clarifying questions related to the pseudopotentials. The authors thank Federico Grasselli and Stefano Baroni for providing data and notebooks for computing the conductivity of a molten salt. This research used the Savio computational cluster resource provided by the Berkeley Research Computing program at the University of California, Berkeley (supported by the UC Berkeley Chancellor, Vice Chancellor for Research, and Chief Information Officer). D.S.K. and P.Z. acknowledge funding from the BIDMaP Postdoctoral Fellowship.","publication_identifier":{"eissn":["2057-3960"]},"volume":11,"OA_place":"publisher","quality_controlled":"1","PlanS_conform":"1","OA_type":"gold","language":[{"iso":"eng"}],"file":[{"file_name":"2025_npj_Zhong.pdf","content_type":"application/pdf","access_level":"open_access","file_id":"21005","relation":"main_file","date_created":"2026-01-20T07:22:04Z","success":1,"creator":"dernst","file_size":2686255,"checksum":"cc999804ba3bfed809ae46c73869e4e3","date_updated":"2026-01-20T07:22:04Z"}],"fulldoi":"https://doi.org/10.1038/s41524-025-01911-z","day":"29","date_published":"2025-12-29T00:00:00Z","tmp":{"name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","image":"/images/cc_by.png","short":"CC BY (4.0)"},"supplementarymaterial":"no","license":"https://creativecommons.org/licenses/by/4.0/","date_updated":"2026-08-07T09:38:09Z","title":"Machine learning interatomic potential can infer electrical response","abstract":[{"text":"Modeling the response of material and chemical systems to electric fields remains a longstanding challenge. Machine learning interatomic potentials (MLIPs) offer an efficient and scalable alternative to quantum mechanical methods, but do not by themselves incorporate electrical response. Here, we show that polarization and Born effective charge (BEC) tensors can be directly extracted from long-range MLIPs within the Latent Ewald Summation (LES) framework, solely by learning from energy and force data. Using this approach, we predict the infrared spectra of bulk water under zero or finite external electric fields, ionic conductivities of high-pressure superionic ice, and the phase transition and hysteresis in ferroelectric PbTiO3 perovskite. This work thus extends the capability of MLIPs to predict electrical response –without training on charges or polarization or BECs– and enables accurate modeling of electric-field-driven processes in diverse systems at scale.","lang":"eng"}],"file_date_updated":"2026-01-20T07:22:04Z","user_id":"317138e5-6ab7-11ef-aa6d-ffef3953e345","publisher":"Springer Nature","doi":"10.1038/s41524-025-01911-z","_id":"20990","status":"public","type":"journal_article"},{"article_processing_charge":"No","year":"2025","oa":1,"issue":"41","oa_version":"Published Version","intvolume":"       122","month":"10","article_type":"original","publication_status":"published","author":[{"last_name":"Zeng","id":"54a2c730-803f-11ed-ab7e-95b29d2680e7","full_name":"Zeng, Zezhu","orcid":"0000-0001-5126-4928","first_name":"Zezhu"},{"last_name":"Fan","first_name":"Zheyong","full_name":"Fan, Zheyong"},{"last_name":"Simoncelli","full_name":"Simoncelli, Michele","first_name":"Michele"},{"first_name":"Chen","full_name":"Chen, Chen","last_name":"Chen"},{"first_name":"Ting","full_name":"Liang, Ting","last_name":"Liang"},{"first_name":"Yue","full_name":"Chen, Yue","last_name":"Chen"},{"last_name":"Thornton","full_name":"Thornton, Geoff","first_name":"Geoff"},{"last_name":"Cheng","full_name":"Cheng, Bingqing","orcid":"0000-0002-3584-9632","first_name":"Bingqing","id":"cbe3cda4-d82c-11eb-8dc7-8ff94289fcc9"}],"ddc":["540"],"dataavailabilitystatement":"Primitive Data Types have been deposited in GitHub (Cs3Bi2I6Cl3_heat_conductivity) (https://github.com/ZengZezhu/Cs3Bi2I6Cl3_heat_conductivity) (74).","acknowledgement":"Z.Z. acknowledges the European Union’s Horizon2020 research and innovation programme under the Marie Skłodowska-Curie Grant Agreement No. 101034413. We acknowledge the high-performance computing facilities offered by Institute of Science and Technology Austria and The University of Hong Kong.","OA_type":"hybrid","PlanS_conform":"1","OA_place":"publisher","quality_controlled":"1","publication_identifier":{"eissn":["1091-6490"]},"volume":122,"ec_funded":1,"related_material":{"link":[{"relation":"software","url":"https://github.com/ZengZezhu/Cs3Bi2I6Cl3_heat_conductivity"}]},"acknowledged_ssus":[{"_id":"ScienComp"}],"has_accepted_license":"1","corr_author":"1","citation":{"ama":"Zeng Z, Fan Z, Simoncelli M, et al. Lattice distortion leads to glassy thermal transport in crystalline Cs3Bi2I6Cl3. <i>Proceedings of the National Academy of Sciences</i>. 2025;122(41):e2415664122. doi:<a href=\"https://doi.org/10.1073/pnas.2415664122\">10.1073/pnas.2415664122</a>","mla":"Zeng, Zezhu, et al. “Lattice Distortion Leads to Glassy Thermal Transport in Crystalline Cs3Bi2I6Cl3.” <i>Proceedings of the National Academy of Sciences</i>, vol. 122, no. 41, National Academy of Sciences, 2025, p. e2415664122, doi:<a href=\"https://doi.org/10.1073/pnas.2415664122\">10.1073/pnas.2415664122</a>.","ista":"Zeng Z, Fan Z, Simoncelli M, Chen C, Liang T, Chen Y, Thornton G, Cheng B. 2025. Lattice distortion leads to glassy thermal transport in crystalline Cs3Bi2I6Cl3. Proceedings of the National Academy of Sciences. 122(41), e2415664122.","chicago":"Zeng, Zezhu, Zheyong Fan, Michele Simoncelli, Chen Chen, Ting Liang, Yue Chen, Geoff Thornton, and Bingqing Cheng. “Lattice Distortion Leads to Glassy Thermal Transport in Crystalline Cs3Bi2I6Cl3.” <i>Proceedings of the National Academy of Sciences</i>. National Academy of Sciences, 2025. <a href=\"https://doi.org/10.1073/pnas.2415664122\">https://doi.org/10.1073/pnas.2415664122</a>.","short":"Z. Zeng, Z. Fan, M. Simoncelli, C. Chen, T. Liang, Y. Chen, G. Thornton, B. Cheng, Proceedings of the National Academy of Sciences 122 (2025) e2415664122.","ieee":"Z. Zeng <i>et al.</i>, “Lattice distortion leads to glassy thermal transport in crystalline Cs3Bi2I6Cl3,” <i>Proceedings of the National Academy of Sciences</i>, vol. 122, no. 41. National Academy of Sciences, p. e2415664122, 2025.","apa":"Zeng, Z., Fan, Z., Simoncelli, M., Chen, C., Liang, T., Chen, Y., … Cheng, B. (2025). Lattice distortion leads to glassy thermal transport in crystalline Cs3Bi2I6Cl3. <i>Proceedings of the National Academy of Sciences</i>. National Academy of Sciences. <a href=\"https://doi.org/10.1073/pnas.2415664122\">https://doi.org/10.1073/pnas.2415664122</a>"},"researchdata_availability":"no","publication":"Proceedings of the National Academy of Sciences","scopus_import":"1","department":[{"_id":"BiCh"}],"date_created":"2025-10-19T22:01:31Z","das_tickbox":"1","tmp":{"name":"Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)","short":"CC BY-NC-ND (4.0)","legal_code_url":"https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode","image":"/images/cc_by_nc_nd.png"},"supplementarymaterial":"no","fulldoi":"https://doi.org/10.1073/pnas.2415664122","file":[{"content_type":"application/pdf","file_name":"2025_PNAS_Zeng.pdf","access_level":"open_access","file_id":"20513","date_created":"2025-10-21T10:02:15Z","relation":"main_file","creator":"dernst","success":1,"file_size":12244843,"checksum":"3f9cd0d67ffe9110fb238407671584b7","date_updated":"2025-10-21T10:02:15Z"}],"isi":1,"language":[{"iso":"eng"}],"external_id":{"pmid":["41052324"],"isi":["001600415200001"]},"date_published":"2025-10-14T00:00:00Z","day":"14","doi":"10.1073/pnas.2415664122","publisher":"National Academy of Sciences","user_id":"317138e5-6ab7-11ef-aa6d-ffef3953e345","project":[{"grant_number":"101034413","_id":"fc2ed2f7-9c52-11eb-aca3-c01059dda49c","call_identifier":"H2020","name":"IST-BRIDGE: International postdoctoral program"}],"type":"journal_article","status":"public","_id":"20492","date_updated":"2026-08-07T10:11:03Z","page":"e2415664122","pmid":1,"file_date_updated":"2025-10-21T10:02:15Z","abstract":[{"lang":"eng","text":"The glassy thermal conductivities observed in crystalline inorganic perovskites such as Cs3Bi2I6Cl3 are perplexing and lacking theoretical explanations. Here, we first experimentally measure its thermal transport behavior from 20 to 300 K, after synthesizing Cs3Bi2I6Cl3 single crystals. Using path-integral molecular dynamics simulations driven by machine learning potentials, we reveal that Cs3Bi2I6Cl3 has large lattice distortions at low temperatures, which may be related to the large atomic size mismatch. Employing the Wigner formulation of thermal transport, we reproduce theexperimental thermal conductivities based on lattice-distorted structures. This studythus provides a framework for predicting and understanding glassy thermal transportin materials with strong lattice disorder."}],"title":"Lattice distortion leads to glassy thermal transport in crystalline Cs3Bi2I6Cl3"},{"tmp":{"name":"Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)","short":"CC BY-NC-ND (4.0)","legal_code_url":"https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode","image":"/images/cc_by_nc_nd.png"},"supplementarymaterial":"no","external_id":{"isi":["001520226300001"]},"language":[{"iso":"eng"}],"isi":1,"fulldoi":"https://doi.org/10.1021/acsmaterialslett.5c00263","file":[{"success":1,"creator":"dernst","relation":"main_file","date_created":"2025-12-30T09:13:06Z","date_updated":"2025-12-30T09:13:06Z","checksum":"d61e63439ddeaef29e9a2ee0f65c4ec1","file_size":2402059,"content_type":"application/pdf","file_name":"2025_ACSMaterialsLetters_Zeng.pdf","file_id":"20903","access_level":"open_access"}],"day":"30","date_published":"2025-06-30T00:00:00Z","user_id":"317138e5-6ab7-11ef-aa6d-ffef3953e345","publisher":"American Chemical Society","doi":"10.1021/acsmaterialslett.5c00263","_id":"20011","status":"public","project":[{"_id":"fc2ed2f7-9c52-11eb-aca3-c01059dda49c","name":"IST-BRIDGE: International postdoctoral program","call_identifier":"H2020","grant_number":"101034413"}],"type":"journal_article","page":"2695-2701","date_updated":"2026-08-07T10:06:48Z","title":"Thermal transport of amorphous hafnia across the glass transition","abstract":[{"text":"Heat transport in glasses over a wide temperature range is critical for applications in gate dielectrics and thermal insulators but remains poorly understood due to the challenges in modeling vibrational anharmonicity and configurational dynamics across the glass transition. Recent predictions show an unusual decrease in thermal conductivity (κ) with temperature in amorphous hafnia (a-HfO2), contrasting with the typical trend in glasses. Using molecular dynamics with a machine-learning-based neuroevolution potential, we compute κ of a-HfO2 from 50 K to 2000 K. At low temperatures, the Wigner transport equation captures both anharmonicity and quantum statistics. Above 1200 K, atomic diffusion invalidates the quasiparticle picture, and we resort to the Green–Kubo method to capture convective transport. We further extend the Wigner transport equation to supercooled a-HfO2, revealing the crucial role of low-frequency modes in facilitating heat transport. The computed κ, based on both Green–Kubo and Wigner transport theories, increases continuously with temperature up to 2000 K.","lang":"eng"}],"file_date_updated":"2025-12-30T09:13:06Z","oa":1,"year":"2025","article_processing_charge":"Yes (in subscription journal)","month":"06","oa_version":"Published Version","author":[{"last_name":"Zeng","full_name":"Zeng, Zezhu","orcid":"0000-0001-5126-4928","first_name":"Zezhu","id":"54a2c730-803f-11ed-ab7e-95b29d2680e7"},{"last_name":"Liang","full_name":"Liang, Xia","first_name":"Xia"},{"full_name":"Fan, Zheyong","first_name":"Zheyong","last_name":"Fan"},{"last_name":"Chen","first_name":"Yue","full_name":"Chen, Yue"},{"last_name":"Simoncelli","first_name":"Michele","full_name":"Simoncelli, Michele"},{"id":"cbe3cda4-d82c-11eb-8dc7-8ff94289fcc9","first_name":"Bingqing","orcid":"0000-0002-3584-9632","full_name":"Cheng, Bingqing","last_name":"Cheng"}],"article_type":"original","publication_status":"published","ddc":["530"],"acknowledgement":"We thank Ludovic Berthier for fruitful discussions and Ting Liang for providing the initial structures of a-SiO2. Z.Z. acknowledges funding from the European Union’s Horizon 2020 Research and Innovation Programme, under Marie Skłodowska-Curie grant agreement No. 101034413. The authors also acknowledge the research computing facilities provided by HPC ISTA and ITS HKU.","dataavailabilitystatement":"All necessary source data files generated for this study are available in the GitHub repository https://github.com/ZengZezhu/heat-conductivity-a-HfO2.","publication_identifier":{"eissn":["2639-4979"]},"ec_funded":1,"OA_place":"publisher","quality_controlled":"1","OA_type":"hybrid","citation":{"ista":"Zeng Z, Liang X, Fan Z, Chen Y, Simoncelli M, Cheng B. 2025. Thermal transport of amorphous hafnia across the glass transition. ACS Materials Letters., 2695–2701.","mla":"Zeng, Zezhu, et al. “Thermal Transport of Amorphous Hafnia across the Glass Transition.” <i>ACS Materials Letters</i>, American Chemical Society, 2025, pp. 2695–701, doi:<a href=\"https://doi.org/10.1021/acsmaterialslett.5c00263\">10.1021/acsmaterialslett.5c00263</a>.","ama":"Zeng Z, Liang X, Fan Z, Chen Y, Simoncelli M, Cheng B. Thermal transport of amorphous hafnia across the glass transition. <i>ACS Materials Letters</i>. 2025:2695-2701. doi:<a href=\"https://doi.org/10.1021/acsmaterialslett.5c00263\">10.1021/acsmaterialslett.5c00263</a>","short":"Z. Zeng, X. Liang, Z. Fan, Y. Chen, M. Simoncelli, B. Cheng, ACS Materials Letters (2025) 2695–2701.","ieee":"Z. Zeng, X. Liang, Z. Fan, Y. Chen, M. Simoncelli, and B. Cheng, “Thermal transport of amorphous hafnia across the glass transition,” <i>ACS Materials Letters</i>. American Chemical Society, pp. 2695–2701, 2025.","apa":"Zeng, Z., Liang, X., Fan, Z., Chen, Y., Simoncelli, M., &#38; Cheng, B. (2025). Thermal transport of amorphous hafnia across the glass transition. <i>ACS Materials Letters</i>. American Chemical Society. <a href=\"https://doi.org/10.1021/acsmaterialslett.5c00263\">https://doi.org/10.1021/acsmaterialslett.5c00263</a>","chicago":"Zeng, Zezhu, Xia Liang, Zheyong Fan, Yue Chen, Michele Simoncelli, and Bingqing Cheng. “Thermal Transport of Amorphous Hafnia across the Glass Transition.” <i>ACS Materials Letters</i>. American Chemical Society, 2025. <a href=\"https://doi.org/10.1021/acsmaterialslett.5c00263\">https://doi.org/10.1021/acsmaterialslett.5c00263</a>."},"corr_author":"1","acknowledged_ssus":[{"_id":"ScienComp"}],"has_accepted_license":"1","related_material":{"link":[{"relation":"software","url":"https://github.com/ZengZezhu/heat-conductivity-a-HfO2"}]},"das_tickbox":"1","date_created":"2025-07-13T22:01:24Z","scopus_import":"1","department":[{"_id":"BiCh"}],"publication":"ACS Materials Letters","researchdata_availability":"no"},{"publisher":"Springer Nature","DOAJ_listed":"1","user_id":"317138e5-6ab7-11ef-aa6d-ffef3953e345","doi":"10.1038/s41524-025-01577-7","type":"journal_article","_id":"19495","status":"public","date_updated":"2026-08-07T10:04:17Z","title":"Latent Ewald summation for machine learning of long-range interactions","file_date_updated":"2025-04-08T09:34:58Z","abstract":[{"lang":"eng","text":"Machine learning interatomic potentials (MLIPs) often neglect long-range interactions, such as electrostatic and dispersion forces. In this work, we introduce a straightforward and efficient method to account for long-range interactions by learning a hidden variable from local atomic descriptors and applying an Ewald summation to this variable. We demonstrate that in systems including charged and polar molecular dimers, bulk water, and water-vapor interface, standard short-ranged MLIPs can lead to unphysical predictions even when employing message passing. The long-range models effectively eliminate these artifacts, with only about twice the computational cost of short-range MLIPs."}],"tmp":{"name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","image":"/images/cc_by.png","short":"CC BY (4.0)"},"supplementarymaterial":"no","external_id":{"isi":["001453622900002"],"arxiv":["2408.15165"]},"file":[{"checksum":"cc99b7407a12139d9b2d8457961935ae","date_updated":"2025-04-08T09:34:58Z","file_size":1608315,"success":1,"creator":"dernst","relation":"main_file","date_created":"2025-04-08T09:34:58Z","file_id":"19528","access_level":"open_access","file_name":"2025_npjCompMaterials_Cheng.pdf","content_type":"application/pdf"}],"fulldoi":"https://doi.org/10.1038/s41524-025-01577-7","isi":1,"language":[{"iso":"eng"}],"day":"26","date_published":"2025-03-26T00:00:00Z","acknowledgement":"B. C. thanks David Limmer for providing the water slab dataset, and Carolin Faller for the NaCl dataset.","dataavailabilitystatement":"The training scripts, trained CACE potentials, and MD input files are available at https://github.com/BingqingCheng/cace-lr-fit.","article_number":"80","publication_identifier":{"eissn":["2057-3960"]},"volume":11,"OA_type":"gold","quality_controlled":"1","OA_place":"publisher","arxiv":1,"corr_author":"1","citation":{"ista":"Cheng B. 2025. Latent Ewald summation for machine learning of long-range interactions. npj Computational Materials. 11, 80.","ama":"Cheng B. Latent Ewald summation for machine learning of long-range interactions. <i>npj Computational Materials</i>. 2025;11. doi:<a href=\"https://doi.org/10.1038/s41524-025-01577-7\">10.1038/s41524-025-01577-7</a>","mla":"Cheng, Bingqing. “Latent Ewald Summation for Machine Learning of Long-Range Interactions.” <i>Npj Computational Materials</i>, vol. 11, 80, Springer Nature, 2025, doi:<a href=\"https://doi.org/10.1038/s41524-025-01577-7\">10.1038/s41524-025-01577-7</a>.","ieee":"B. Cheng, “Latent Ewald summation for machine learning of long-range interactions,” <i>npj Computational Materials</i>, vol. 11. Springer Nature, 2025.","apa":"Cheng, B. (2025). Latent Ewald summation for machine learning of long-range interactions. <i>Npj Computational Materials</i>. Springer Nature. <a href=\"https://doi.org/10.1038/s41524-025-01577-7\">https://doi.org/10.1038/s41524-025-01577-7</a>","short":"B. Cheng, Npj Computational Materials 11 (2025).","chicago":"Cheng, Bingqing. “Latent Ewald Summation for Machine Learning of Long-Range Interactions.” <i>Npj Computational Materials</i>. Springer Nature, 2025. <a href=\"https://doi.org/10.1038/s41524-025-01577-7\">https://doi.org/10.1038/s41524-025-01577-7</a>."},"has_accepted_license":"1","department":[{"_id":"BiCh"}],"scopus_import":"1","das_tickbox":"1","date_created":"2025-04-06T22:01:32Z","researchdata_availability":"unclear","publication":"npj Computational Materials","article_processing_charge":"Yes","year":"2025","oa":1,"month":"03","intvolume":"        11","oa_version":"Published Version","publication_status":"published","article_type":"original","author":[{"id":"cbe3cda4-d82c-11eb-8dc7-8ff94289fcc9","first_name":"Bingqing","orcid":"0000-0002-3584-9632","full_name":"Cheng, Bingqing","last_name":"Cheng"}],"ddc":["000"]},{"abstract":[{"lang":"eng","text":"Qualitative and quantitative orbital properties such as bonding/antibonding character, localization, and orbital energies are critical to how chemists understand reactivity, catalysis, and excited-state behavior. Despite this, representations of orbitals in deep learning models have been very underdeveloped relative to representations of molecular geometries and Hamiltonians. Here, we apply state-of-the-art equivariant deep learning architectures to the task of assigning global labels to orbitals, namely energies characterizations, given the molecular coefficients from Hartree–Fock or density functional theory. The architecture we have developed, the Cartesian Equivariant Orbital Network (CEONET), shows how molecular orbital coefficients are readily featurized as equivariant node features common to all graph-based machine-learned potentials. We find that CEONET performs well at predicting difficult quantitative labels such as the orbital energy and orbital entropy. Furthermore, we find that the CEONET representation provides an intuitive latent space for differentiating orbital character for the qualitative assignment of e.g. bonding or antibonding character. In addition to providing a useful representation for further integrating deep learning with electronic structure theory, we expect CEONET to be useful for automatizing and interpreting the results of advanced electronic structure methods such as complete active space self-consistent field theory. In particular, the ability of CEONET to infer multireference character via the orbital entropy paves the way toward the machine-learned selection of active spaces."}],"file_date_updated":"2025-12-01T08:41:32Z","title":"Cartesian equivariant representations for learning and understanding molecular orbitals","date_updated":"2026-08-07T10:27:53Z","pmid":1,"status":"public","_id":"20702","type":"journal_article","doi":"10.1073/pnas.2510235122","user_id":"317138e5-6ab7-11ef-aa6d-ffef3953e345","publisher":"National Academy of Sciences","date_published":"2025-12-02T00:00:00Z","day":"02","language":[{"iso":"eng"}],"fulldoi":"https://doi.org/10.1073/pnas.2510235122","file":[{"checksum":"58051539a884c7a97306fd3afdb539ac","date_updated":"2025-12-01T08:41:32Z","file_size":27607870,"creator":"dernst","success":1,"date_created":"2025-12-01T08:41:32Z","relation":"main_file","file_id":"20719","access_level":"open_access","content_type":"application/pdf","file_name":"2025_PNAS_King.pdf"}],"external_id":{"pmid":["41269783"]},"supplementarymaterial":"no","tmp":{"name":"Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)","short":"CC BY-NC-ND (4.0)","legal_code_url":"https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode","image":"/images/cc_by_nc_nd.png"},"publication":"Proceedings of the National Academy of Sciences","researchdata_availability":"no","das_tickbox":"1","date_created":"2025-11-30T23:02:06Z","department":[{"_id":"BiCh"}],"scopus_import":"1","has_accepted_license":"1","related_material":{"link":[{"relation":"software","url":"https://github.com/GagliardiGroup/CEONet "}]},"citation":{"mla":"King, Daniel S., et al. “Cartesian Equivariant Representations for Learning and Understanding Molecular Orbitals.” <i>Proceedings of the National Academy of Sciences</i>, vol. 122, no. 48, e2510235122, National Academy of Sciences, 2025, doi:<a href=\"https://doi.org/10.1073/pnas.2510235122\">10.1073/pnas.2510235122</a>.","ama":"King DS, Grzenda D, Zhu R, et al. Cartesian equivariant representations for learning and understanding molecular orbitals. <i>Proceedings of the National Academy of Sciences</i>. 2025;122(48). doi:<a href=\"https://doi.org/10.1073/pnas.2510235122\">10.1073/pnas.2510235122</a>","ista":"King DS, Grzenda D, Zhu R, Hudson N, Foster I, Cheng B, Gagliardi L. 2025. Cartesian equivariant representations for learning and understanding molecular orbitals. Proceedings of the National Academy of Sciences. 122(48), e2510235122.","chicago":"King, Daniel S., Daniel Grzenda, Ray Zhu, Nathaniel Hudson, Ian Foster, Bingqing Cheng, and Laura Gagliardi. “Cartesian Equivariant Representations for Learning and Understanding Molecular Orbitals.” <i>Proceedings of the National Academy of Sciences</i>. National Academy of Sciences, 2025. <a href=\"https://doi.org/10.1073/pnas.2510235122\">https://doi.org/10.1073/pnas.2510235122</a>.","short":"D.S. King, D. Grzenda, R. Zhu, N. Hudson, I. Foster, B. Cheng, L. Gagliardi, Proceedings of the National Academy of Sciences 122 (2025).","apa":"King, D. S., Grzenda, D., Zhu, R., Hudson, N., Foster, I., Cheng, B., &#38; Gagliardi, L. (2025). Cartesian equivariant representations for learning and understanding molecular orbitals. <i>Proceedings of the National Academy of Sciences</i>. National Academy of Sciences. <a href=\"https://doi.org/10.1073/pnas.2510235122\">https://doi.org/10.1073/pnas.2510235122</a>","ieee":"D. S. King <i>et al.</i>, “Cartesian equivariant representations for learning and understanding molecular orbitals,” <i>Proceedings of the National Academy of Sciences</i>, vol. 122, no. 48. National Academy of Sciences, 2025."},"corr_author":"1","quality_controlled":"1","OA_place":"publisher","OA_type":"hybrid","volume":122,"publication_identifier":{"eissn":["1091-6490"]},"article_number":"e2510235122","dataavailabilitystatement":"Code has been deposited to https://github.com/GagliardiGroup/CEONet (83). Data has been deposited to https://doi.org/10.5281/zenodo.16934624 (84).","acknowledgement":"This work is supported as part of the Catalyst Design for Decarbonization Center, an Energy Frontier Research Center funded by the U.S. Department of Energy, Office of Science, Basic Energy Sciences under award no. DE-SC0023383. We thank the Research Computing Center at the University of Chicago and for access to computational resources. Additionally, this research used the Savio computational cluster resource provided by the Berkeley Research Computing program at the University of California (UC), Berkeley (supported by the UC Berkeley Chancellor, Vice Chancellor for Research, and Chief Information Officer). Furthermore, we thank Matthew Hennefarth and Matt Hermes for useful discussions.","ddc":["540"],"author":[{"first_name":"Daniel S.","full_name":"King, Daniel S.","last_name":"King"},{"full_name":"Grzenda, Daniel","first_name":"Daniel","last_name":"Grzenda"},{"full_name":"Zhu, Ray","first_name":"Ray","last_name":"Zhu"},{"last_name":"Hudson","first_name":"Nathaniel","full_name":"Hudson, Nathaniel"},{"first_name":"Ian","full_name":"Foster, Ian","last_name":"Foster"},{"full_name":"Cheng, Bingqing","orcid":"0000-0002-3584-9632","first_name":"Bingqing","id":"cbe3cda4-d82c-11eb-8dc7-8ff94289fcc9","last_name":"Cheng"},{"last_name":"Gagliardi","first_name":"Laura","full_name":"Gagliardi, Laura"}],"publication_status":"published","article_type":"original","oa_version":"Published Version","issue":"48","intvolume":"       122","month":"12","year":"2025","oa":1,"article_processing_charge":"Yes (in subscription journal)"},{"date_published":"2025-10-31T00:00:00Z","day":"31","fulldoi":"https://doi.org/10.1021/acs.jctc.5c01248","isi":1,"language":[{"iso":"eng"}],"external_id":{"isi":["001605927900001"],"pmid":["41172130"]},"supplementarymaterial":"no","abstract":[{"lang":"eng","text":"Generative models have advanced significantly in sampling material systems with continuous variables, such as atomistic structures. However, their application to discrete variables, like atom types or spin states, remains underexplored. In this work, we introduce a discrete flow matching model, tailored for systems with discrete phase-space coordinates (e.g., the Ising model or a multicomponent system on a lattice). This approach enables a single model to sample free energy surfaces over a wide temperature range with minimal training overhead, and the model generation is scalable to larger lattice sizes than those in the training set. We demonstrate our approach on the 2D Ising model, showing efficient and reliable free energy sampling. These results highlight the potential of flow matching for low-cost, scalable free energy sampling in discrete systems and suggest promising extensions to alchemical degrees of freedom in crystalline materials. The codebase developed for this work is openly available at https://github.com/tuoping/alchemicalFES."}],"title":"Scalable multitemperature free energy sampling of classical Ising spin states","page":"11427-11435","date_updated":"2026-08-07T10:29:06Z","pmid":1,"project":[{"_id":"fc2ed2f7-9c52-11eb-aca3-c01059dda49c","name":"IST-BRIDGE: International postdoctoral program","call_identifier":"H2020","grant_number":"101034413"}],"type":"journal_article","_id":"20704","status":"public","doi":"10.1021/acs.jctc.5c01248","publisher":"American Chemical Society","user_id":"317138e5-6ab7-11ef-aa6d-ffef3953e345","publication_status":"published","article_type":"original","author":[{"last_name":"Tuo","full_name":"Tuo, Ping","first_name":"Ping","id":"6e5644c0-c180-11ed-a2da-facc4c9f4f09"},{"first_name":"Zezhu","full_name":"Zeng, Zezhu","orcid":"0000-0001-5126-4928","id":"54a2c730-803f-11ed-ab7e-95b29d2680e7","last_name":"Zeng"},{"last_name":"Chen","id":"4d0a9064-1ff6-11ee-9fa6-ec046c604785","first_name":"Jiale","full_name":"Chen, Jiale","orcid":"0000-0001-5337-5875"},{"first_name":"Bingqing","full_name":"Cheng, Bingqing","orcid":"0000-0002-3584-9632","id":"cbe3cda4-d82c-11eb-8dc7-8ff94289fcc9","last_name":"Cheng"}],"issue":"22","oa_version":"None","intvolume":"        21","month":"10","article_processing_charge":"No","year":"2025","researchdata_availability":"no","publication":"Journal of Chemical Theory and Computation","scopus_import":"1","department":[{"_id":"BiCh"},{"_id":"DaAl"}],"date_created":"2025-11-30T23:02:06Z","das_tickbox":"0","related_material":{"link":[{"relation":"software","url":"https://github.com/tuoping/alchemicalFES"}]},"acknowledged_ssus":[{"_id":"ScienComp"}],"corr_author":"1","citation":{"chicago":"Tuo, Ping, Zezhu Zeng, Jiale Chen, and Bingqing Cheng. “Scalable Multitemperature Free Energy Sampling of Classical Ising Spin States.” <i>Journal of Chemical Theory and Computation</i>. American Chemical Society, 2025. <a href=\"https://doi.org/10.1021/acs.jctc.5c01248\">https://doi.org/10.1021/acs.jctc.5c01248</a>.","apa":"Tuo, P., Zeng, Z., Chen, J., &#38; Cheng, B. (2025). Scalable multitemperature free energy sampling of classical Ising spin states. <i>Journal of Chemical Theory and Computation</i>. American Chemical Society. <a href=\"https://doi.org/10.1021/acs.jctc.5c01248\">https://doi.org/10.1021/acs.jctc.5c01248</a>","ieee":"P. Tuo, Z. Zeng, J. Chen, and B. Cheng, “Scalable multitemperature free energy sampling of classical Ising spin states,” <i>Journal of Chemical Theory and Computation</i>, vol. 21, no. 22. American Chemical Society, pp. 11427–11435, 2025.","short":"P. Tuo, Z. Zeng, J. Chen, B. Cheng, Journal of Chemical Theory and Computation 21 (2025) 11427–11435.","ama":"Tuo P, Zeng Z, Chen J, Cheng B. Scalable multitemperature free energy sampling of classical Ising spin states. <i>Journal of Chemical Theory and Computation</i>. 2025;21(22):11427-11435. doi:<a href=\"https://doi.org/10.1021/acs.jctc.5c01248\">10.1021/acs.jctc.5c01248</a>","mla":"Tuo, Ping, et al. “Scalable Multitemperature Free Energy Sampling of Classical Ising Spin States.” <i>Journal of Chemical Theory and Computation</i>, vol. 21, no. 22, American Chemical Society, 2025, pp. 11427–35, doi:<a href=\"https://doi.org/10.1021/acs.jctc.5c01248\">10.1021/acs.jctc.5c01248</a>.","ista":"Tuo P, Zeng Z, Chen J, Cheng B. 2025. Scalable multitemperature free energy sampling of classical Ising spin states. Journal of Chemical Theory and Computation. 21(22), 11427–11435."},"OA_type":"closed access","quality_controlled":"1","publication_identifier":{"issn":["1549-9618"],"eissn":["1549-9626"]},"volume":21,"ec_funded":1,"acknowledgement":"P.T. acknowledges funding from FFG MAGNIFICO and the BIDMaP Postdoctoral Fellowship. Z.Z. acknowledges funding from the European Union’s Horizon 2020 research and innovation program under the Marie Skłodowska-Curie grant agreement No. 101034413. The authors acknowledge the research computing facilities provided by the Institute of Science and Technology Austria (ISTA), and resources of the National Energy Research Scientific Computing Center (NERSC), a Department of Energy Office of Science User Facility using NERSC award DOEERCAP0031751 ’GenAI@NERSC’. P.T. acknowledges valued discussions with Dr. Daniel King, Dr. Lei Wang, and Dr. Fuzhi Dai."},{"pmid":1,"date_updated":"2026-08-27T12:16:47Z","title":"Machine learning of charges and long-range interactions from energies and forces","file_date_updated":"2025-10-13T07:54:51Z","abstract":[{"lang":"eng","text":"Accurate modeling of long-range forces is critical in atomistic simulations, as they play a central role in determining the properties of material and chemical systems. However, standard machine learning interatomic potentials (MLIPs) often rely on short-range approximations, limiting their applicability to systems with significant electrostatics and dispersion forces. We recently introduced the Latent Ewald Summation (LES) method, which captures long-range electrostatics without explicitly learning atomic charges or charge equilibration. We benchmark LES on diverse and challenging systems, including charged molecules, ionic liquids, electrolyte solutions, polar dipeptides, surface adsorption, electrolyte/solid interfaces, and solid-solid interfaces. Here we show that LES can reproduce the exact atomic charges for classical systems with fixed charges and can infer dipole and quadrupole moments, as well as the dipole derivative with respect to atomic positions, for quantum mechanical systems. Moreover, LES can achieve better accuracy in energy and force predictions compared to methods that explicitly learn from charges."}],"publisher":"Springer Nature","DOAJ_listed":"1","user_id":"317138e5-6ab7-11ef-aa6d-ffef3953e345","doi":"10.1038/s41467-025-63852-x","type":"journal_article","status":"public","_id":"20452","external_id":{"pmid":["41034200"],"isi":["001586620700015"]},"fulldoi":"https://doi.org/10.1038/s41467-025-63852-x","isi":1,"file":[{"success":1,"creator":"dernst","relation":"main_file","date_created":"2025-10-13T07:54:51Z","date_updated":"2025-10-13T07:54:51Z","checksum":"34b6005d349bbff85839c4e51d6c8725","file_size":4907055,"file_name":"2025_NatureComm_King.pdf","content_type":"application/pdf","file_id":"20460","access_level":"open_access"}],"language":[{"iso":"eng"}],"day":"01","date_published":"2025-10-01T00:00:00Z","tmp":{"name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","image":"/images/cc_by.png","short":"CC BY (4.0)"},"supplementarymaterial":"no","corr_author":"1","citation":{"ista":"King DS, Kim D, Zhong P, Cheng B. 2025. Machine learning of charges and long-range interactions from energies and forces. Nature Communications. 16, 8763.","mla":"King, Daniel S., et al. “Machine Learning of Charges and Long-Range Interactions from Energies and Forces.” <i>Nature Communications</i>, vol. 16, 8763, Springer Nature, 2025, doi:<a href=\"https://doi.org/10.1038/s41467-025-63852-x\">10.1038/s41467-025-63852-x</a>.","ama":"King DS, Kim D, Zhong P, Cheng B. Machine learning of charges and long-range interactions from energies and forces. <i>Nature Communications</i>. 2025;16. doi:<a href=\"https://doi.org/10.1038/s41467-025-63852-x\">10.1038/s41467-025-63852-x</a>","apa":"King, D. S., Kim, D., Zhong, P., &#38; Cheng, B. (2025). Machine learning of charges and long-range interactions from energies and forces. <i>Nature Communications</i>. Springer Nature. <a href=\"https://doi.org/10.1038/s41467-025-63852-x\">https://doi.org/10.1038/s41467-025-63852-x</a>","ieee":"D. S. King, D. Kim, P. Zhong, and B. Cheng, “Machine learning of charges and long-range interactions from energies and forces,” <i>Nature Communications</i>, vol. 16. Springer Nature, 2025.","short":"D.S. King, D. Kim, P. Zhong, B. Cheng, Nature Communications 16 (2025).","chicago":"King, Daniel S., Dongjin Kim, Peichen Zhong, and Bingqing Cheng. “Machine Learning of Charges and Long-Range Interactions from Energies and Forces.” <i>Nature Communications</i>. Springer Nature, 2025. <a href=\"https://doi.org/10.1038/s41467-025-63852-x\">https://doi.org/10.1038/s41467-025-63852-x</a>."},"related_material":{"record":[{"relation":"research_data","status":"public","id":"22770"}]},"has_accepted_license":"1","department":[{"_id":"BiCh"}],"scopus_import":"1","date_created":"2025-10-12T22:01:25Z","das_tickbox":"1","researchdata_availability":"no","publication":"Nature Communications","acknowledgement":"We thank Chunyi Zhang for providing the TiO2(101)/NaCl+NaOH+HCl(aq) dataset and for useful discussions. We thank Jia-Xin Zhu for providing the Pt(111)/KF(aq) dataset. We thank Tsz Wai Ko and Jonas Finkler for useful discussions and for the DFT-optimized Au2-MgO(001) structures. We thank Junmin Chen for discussions. D.K and B.C. acknowledge funding from Toyota Research Institute Synthesis Advanced Research Challenge. D.S.K. and P.Z. acknowledge funding from BIDMaP Postdoctoral Fellowship.","dataavailabilitystatement":"The training sets, training scripts, MD input files, and trained CACE potentials are available at https://github.com/BingqingCheng/cace-lr-fit; see https://doi.org/10.5281/zenodo.16415061. Source data for all figures are provided with this paper. Source data are provided with this paper.","article_number":"8763","publication_identifier":{"eissn":["2041-1723"]},"volume":16,"OA_type":"gold","PlanS_conform":"1","OA_place":"publisher","quality_controlled":"1","article_type":"original","publication_status":"published","author":[{"last_name":"King","full_name":"King, Daniel S.","first_name":"Daniel S."},{"last_name":"Kim","first_name":"Dongjin","full_name":"Kim, Dongjin"},{"last_name":"Zhong","full_name":"Zhong, Peichen","first_name":"Peichen"},{"last_name":"Cheng","first_name":"Bingqing","full_name":"Cheng, Bingqing","orcid":"0000-0002-3584-9632","id":"cbe3cda4-d82c-11eb-8dc7-8ff94289fcc9"}],"ddc":["000"],"article_processing_charge":"Yes","year":"2025","oa":1,"month":"10","intvolume":"        16","oa_version":"Published Version"},{"publication_status":"published","article_type":"original","author":[{"id":"8dff9c62-32b0-11ee-9fa8-fc73025e10f3","first_name":"Xiaoyu","full_name":"Wang, Xiaoyu","last_name":"Wang"},{"last_name":"Cheng","id":"cbe3cda4-d82c-11eb-8dc7-8ff94289fcc9","first_name":"Bingqing","full_name":"Cheng, Bingqing","orcid":"0000-0002-3584-9632"}],"month":"07","oa_version":"Preprint","issue":"3","intvolume":"       161","main_file_link":[{"url":"https://doi.org/10.48550/arXiv.2405.02216","open_access":"1"}],"article_processing_charge":"No","oa":1,"year":"2024","scopus_import":"1","department":[{"_id":"BiCh"},{"_id":"GradSch"}],"date_created":"2024-07-21T22:01:00Z","das_tickbox":"1","researchdata_availability":"no","publication":"Journal of Chemical Physics","corr_author":"1","citation":{"ista":"Wang X, Cheng B. 2024. Integrating molecular dynamics simulations and experimental data for azeotrope predictions in binary mixtures. Journal of Chemical Physics. 161(3), 034111.","ama":"Wang X, Cheng B. Integrating molecular dynamics simulations and experimental data for azeotrope predictions in binary mixtures. <i>Journal of Chemical Physics</i>. 2024;161(3). doi:<a href=\"https://doi.org/10.1063/5.0217232\">10.1063/5.0217232</a>","mla":"Wang, Xiaoyu, and Bingqing Cheng. “Integrating Molecular Dynamics Simulations and Experimental Data for Azeotrope Predictions in Binary Mixtures.” <i>Journal of Chemical Physics</i>, vol. 161, no. 3, 034111, AIP Publishing, 2024, doi:<a href=\"https://doi.org/10.1063/5.0217232\">10.1063/5.0217232</a>.","short":"X. Wang, B. Cheng, Journal of Chemical Physics 161 (2024).","ieee":"X. Wang and B. Cheng, “Integrating molecular dynamics simulations and experimental data for azeotrope predictions in binary mixtures,” <i>Journal of Chemical Physics</i>, vol. 161, no. 3. AIP Publishing, 2024.","apa":"Wang, X., &#38; Cheng, B. (2024). Integrating molecular dynamics simulations and experimental data for azeotrope predictions in binary mixtures. <i>Journal of Chemical Physics</i>. AIP Publishing. <a href=\"https://doi.org/10.1063/5.0217232\">https://doi.org/10.1063/5.0217232</a>","chicago":"Wang, Xiaoyu, and Bingqing Cheng. “Integrating Molecular Dynamics Simulations and Experimental Data for Azeotrope Predictions in Binary Mixtures.” <i>Journal of Chemical Physics</i>. AIP Publishing, 2024. <a href=\"https://doi.org/10.1063/5.0217232\">https://doi.org/10.1063/5.0217232</a>."},"related_material":{"link":[{"relation":"software","url":"https://github.com/Xiaoyu-Wang-Stone/Azeotrope_S0"}]},"volume":161,"publication_identifier":{"eissn":["1089-7690"],"issn":["0021-9606"]},"quality_controlled":"1","arxiv":1,"acknowledgement":"B.C. thanks Alessandro Laio, who introduced the phenomenon of azeotrope and suggested using the S0 method to compute it. B.C. and X.W. thank Felix Wodaczek for the insightful comments and suggestions on the manuscript. B.C. and X.W. acknowledge the resources provided by the Cambridge Tier-2 system operated by the University of Cambridge Research Computing Service, funded by EPSRC Tier-2 capital (Grant No. EP/P020259/1).","dataavailabilitystatement":"All simulation setups, analysis scripts, and raw data in the study are available in the SI repository https://github.com/Xiaoyu-Wang-Stone/Azeotrope_S0.","article_number":"034111","date_published":"2024-07-14T00:00:00Z","day":"14","external_id":{"pmid":["39007379"],"isi":["001281819100016"],"arxiv":["2405.02216"]},"fulldoi":"https://doi.org/10.1063/5.0217232","isi":1,"language":[{"iso":"eng"}],"supplementarymaterial":"no","title":"Integrating molecular dynamics simulations and experimental data for azeotrope predictions in binary mixtures","abstract":[{"lang":"eng","text":"An azeotrope is a constant boiling point mixture, and its behavior is important for fluid separation processes. Predicting azeotropes from atomistic simulations is difficult due to the complexities and convergence problems of Monte Carlo and free-energy perturbation techniques. Here, we present a methodology for predicting the azeotropes of binary mixtures, which computes the compositional dependence of chemical potentials from molecular dynamics simulations using the S0 method and employs experimental boiling point and vaporization enthalpy data. Using this methodology, we reproduce the azeotropes, or lack thereof, in five case studies, including ethanol/water, ethanol/isooctane, methanol/water, hydrazine/water, and acetone/chloroform mixtures. We find that it is crucial to use the experimental boiling point and vaporization enthalpy for reliable azeotrope predictions, as empirical force fields are not accurate enough for these quantities. Finally, we use regular solution models to rationalize the azeotropes and reveal that they tend to form when the mixture components have similar boiling points and strong interactions."}],"pmid":1,"date_updated":"2026-08-07T10:38:59Z","type":"journal_article","_id":"17278","status":"public","publisher":"AIP Publishing","user_id":"317138e5-6ab7-11ef-aa6d-ffef3953e345","doi":"10.1063/5.0217232"},{"has_accepted_license":"1","citation":{"ista":"Hunnisett LM et al. 2024. The seventh blind test of crystal structure prediction: Structure generation methods. Acta Crystallographica Section B. 80(6), 517–547.","mla":"Hunnisett, Lily M., et al. “The Seventh Blind Test of Crystal Structure Prediction: Structure Generation Methods.” <i>Acta Crystallographica Section B</i>, vol. 80, no. 6, International Union of Crystallography, 2024, pp. 517–47, doi:<a href=\"https://doi.org/10.1107/s2052520624007492\">10.1107/s2052520624007492</a>.","ama":"Hunnisett LM, Nyman J, Francia N, et al. The seventh blind test of crystal structure prediction: Structure generation methods. <i>Acta Crystallographica Section B</i>. 2024;80(6):517-547. doi:<a href=\"https://doi.org/10.1107/s2052520624007492\">10.1107/s2052520624007492</a>","short":"L.M. Hunnisett, J. Nyman, N. Francia, N.S. Abraham, C.S. Adjiman, S. Aitipamula, T. Alkhidir, M. Almehairbi, A. Anelli, D.M. Anstine, J.E. Anthony, J.E. Arnold, F. Bahrami, M.A. Bellucci, R.M. Bhardwaj, I. Bier, J.A. Bis, A.D. Boese, D.H. Bowskill, J. Bramley, J.G. Brandenburg, D.E. Braun, P.W.V. Butler, J. Cadden, S. Carino, E.J. Chan, C. Chang, B. Cheng, S.M. Clarke, S.J. Coles, R.I. Cooper, R. Couch, R. Cuadrado, T. Darden, G.M. Day, H. Dietrich, Y. Ding, A. DiPasquale, B. Dhokale, B.P. van Eijck, M.R.J. Elsegood, D. Firaha, W. Fu, K. Fukuzawa, J. Glover, H. Goto, C. Greenwell, R. Guo, J. Harter, J. Helfferich, D.W.M. Hofmann, J. Hoja, J. Hone, R. Hong, G. Hutchison, Y. Ikabata, O. Isayev, O. Ishaque, V. Jain, Y. Jin, A. Jing, E.R. Johnson, I. Jones, K.V.J. Jose, E.A. Kabova, A. Keates, P.F. Kelly, D. Khakimov, S. Konstantinopoulos, L.N. Kuleshova, H. Li, X. Lin, A. List, C. Liu, Y.M. Liu, Z. Liu, Z.-P. Liu, J.W. Lubach, N. Marom, A.A. Maryewski, H. Matsui, A. Mattei, R.A. Mayo, J.W. Melkumov, S. Mohamed, Z. Momenzadeh Abardeh, H.S. Muddana, N. Nakayama, K.S. Nayal, M.A. Neumann, R. Nikhar, S. Obata, D. O’Connor, A.R. Oganov, K. Okuwaki, A. Otero-de-la-Roza, C.C. Pantelides, S. Parkin, C.J. Pickard, L. Pilia, T. Pivina, R. Podeszwa, A.J.A. Price, L.S. Price, S.L. Price, M.R. Probert, A. Pulido, G.R. Ramteke, A.U. Rehman, S.M. Reutzel-Edens, J. Rogal, M.J. Ross, A.F. Rumson, G. Sadiq, Z.M. Saeed, A. Salimi, M. Salvalaglio, L. Sanders de Almada, K. Sasikumar, S. Sekharan, C. Shang, K. Shankland, K. Shinohara, B. Shi, X. Shi, A.G. Skillman, H. Song, N. Strasser, J. van de Streek, I.J. Sugden, G. Sun, K. Szalewicz, B.I. Tan, L. Tan, F. Tarczynski, C.R. Taylor, A. Tkatchenko, R. Tom, M.E. Tuckerman, Y. Utsumi, L. Vogt-Maranto, J. Weatherston, L.J. Wilkinson, R.D. Willacy, L. Wojtas, G.R. Woollam, Z. Yang, E. Yonemochi, X. Yue, Q. Zeng, Y. Zhang, T. Zhou, Y. Zhou, R. Zubatyuk, J.C. Cole, Acta Crystallographica Section B 80 (2024) 517–547.","apa":"Hunnisett, L. M., Nyman, J., Francia, N., Abraham, N. S., Adjiman, C. S., Aitipamula, S., … Cole, J. C. (2024). The seventh blind test of crystal structure prediction: Structure generation methods. <i>Acta Crystallographica Section B</i>. International Union of Crystallography. <a href=\"https://doi.org/10.1107/s2052520624007492\">https://doi.org/10.1107/s2052520624007492</a>","ieee":"L. M. Hunnisett <i>et al.</i>, “The seventh blind test of crystal structure prediction: Structure generation methods,” <i>Acta Crystallographica Section B</i>, vol. 80, no. 6. International Union of Crystallography, pp. 517–547, 2024.","chicago":"Hunnisett, Lily M., Jonas Nyman, Nicholas Francia, Nathan S. Abraham, Claire S. Adjiman, Srinivasulu Aitipamula, Tamador Alkhidir, et al. “The Seventh Blind Test of Crystal Structure Prediction: Structure Generation Methods.” <i>Acta Crystallographica Section B</i>. International Union of Crystallography, 2024. <a href=\"https://doi.org/10.1107/s2052520624007492\">https://doi.org/10.1107/s2052520624007492</a>."},"publication":"Acta Crystallographica Section B","researchdata_availability":"no","date_created":"2025-01-29T11:07:36Z","das_tickbox":"0","scopus_import":"1","department":[{"_id":"BiCh"}],"acknowledgement":"The CCDC Blind Test Team. The CCDC organizers (L. M. Hunnisett, J. Nyman, N. Francia, I. Sugden, G. Sadiq, and J. C. Cole) gratefully acknowledge numerous CCDC colleagues for\r\ntheir helpful feedback and suggestions on the manuscript (P. McCabe, E. Pidcock, P. Martinez-Bulit, C. Kingsbury), providing useful python knowledge (A. Moldovan), providing and maintaining internal compute resources (K. Taylor, M. Burling, J. Swift, L. Wallis), monitoring and depositing structures in the CSD (S. Ward, K. Orzechowska, V. Menon), support in organization of the blind test meeting (E. Clarke),and improvements to the Crystal Packing Similarity tool (M.\r\nRead). Data analysis was performed using resources provided by the Cambridge Service for Data Driven Discovery (CSD3) operated by the University of Cambridge Research Computing Service (www.csd3.cam.ac.uk), provided by Dell EMC and Intel using Tier-2 funding from the Engineering and Physical Sciences Research Council (capital grant EP/T022159/1), and DiRAC funding from the Science and Technology Facilities Council (www.dirac.ac.uk). N. Francia  thanks M. Salvalaglio for advice on the metadynamics simulations and the University College London for providing access to the Kathleen High Performance Computing Facility Kathleen@UCL) on which simulations were performed. N. Francia also thanks V. Kurlin and D. E. Widdowson for counselling on crystal structure similarity. I. Sugden and N. Francia participated in the blind test as members of Groups 1 and 24, respectively. They were involved in the analysis of the results.\r\nand in writing this paper only after all results were made\r\navailable to participants.","OA_place":"publisher","quality_controlled":"1","OA_type":"hybrid","volume":80,"publication_identifier":{"issn":["2052-5206"]},"author":[{"full_name":"Hunnisett, Lily M.","first_name":"Lily M.","last_name":"Hunnisett"},{"last_name":"Nyman","first_name":"Jonas","full_name":"Nyman, Jonas"},{"last_name":"Francia","first_name":"Nicholas","full_name":"Francia, Nicholas"},{"last_name":"Abraham","first_name":"Nathan S.","full_name":"Abraham, Nathan S."},{"last_name":"Adjiman","full_name":"Adjiman, Claire S.","first_name":"Claire S."},{"last_name":"Aitipamula","first_name":"Srinivasulu","full_name":"Aitipamula, Srinivasulu"},{"last_name":"Alkhidir","first_name":"Tamador","full_name":"Alkhidir, Tamador"},{"last_name":"Almehairbi","first_name":"Mubarak","full_name":"Almehairbi, Mubarak"},{"full_name":"Anelli, Andrea","first_name":"Andrea","last_name":"Anelli"},{"last_name":"Anstine","first_name":"Dylan M.","full_name":"Anstine, Dylan M."},{"last_name":"Anthony","full_name":"Anthony, John E.","first_name":"John E."},{"last_name":"Arnold","first_name":"Joseph E.","full_name":"Arnold, Joseph E."},{"full_name":"Bahrami, Faezeh","first_name":"Faezeh","last_name":"Bahrami"},{"first_name":"Michael A.","full_name":"Bellucci, Michael A.","last_name":"Bellucci"},{"last_name":"Bhardwaj","full_name":"Bhardwaj, Rajni M.","first_name":"Rajni M."},{"first_name":"Imanuel","full_name":"Bier, Imanuel","last_name":"Bier"},{"full_name":"Bis, Joanna A.","first_name":"Joanna A.","last_name":"Bis"},{"last_name":"Boese","full_name":"Boese, A. Daniel","first_name":"A. Daniel"},{"last_name":"Bowskill","full_name":"Bowskill, David H.","first_name":"David H."},{"last_name":"Bramley","first_name":"James","full_name":"Bramley, James"},{"last_name":"Brandenburg","full_name":"Brandenburg, Jan Gerit","first_name":"Jan Gerit"},{"last_name":"Braun","first_name":"Doris E.","full_name":"Braun, Doris E."},{"full_name":"Butler, Patrick W. V.","first_name":"Patrick W. V.","last_name":"Butler"},{"full_name":"Cadden, Joseph","first_name":"Joseph","last_name":"Cadden"},{"last_name":"Carino","first_name":"Stephen","full_name":"Carino, Stephen"},{"last_name":"Chan","full_name":"Chan, Eric J.","first_name":"Eric J."},{"last_name":"Chang","full_name":"Chang, Chao","first_name":"Chao"},{"last_name":"Cheng","id":"cbe3cda4-d82c-11eb-8dc7-8ff94289fcc9","orcid":"0000-0002-3584-9632","full_name":"Cheng, Bingqing","first_name":"Bingqing"},{"last_name":"Clarke","full_name":"Clarke, Sarah M.","first_name":"Sarah M."},{"last_name":"Coles","first_name":"Simon J.","full_name":"Coles, Simon J."},{"last_name":"Cooper","first_name":"Richard I.","full_name":"Cooper, Richard I."},{"last_name":"Couch","first_name":"Ricky","full_name":"Couch, Ricky"},{"first_name":"Ramon","full_name":"Cuadrado, Ramon","last_name":"Cuadrado"},{"first_name":"Tom","full_name":"Darden, Tom","last_name":"Darden"},{"full_name":"Day, Graeme M.","first_name":"Graeme M.","last_name":"Day"},{"last_name":"Dietrich","first_name":"Hanno","full_name":"Dietrich, Hanno"},{"first_name":"Yiming","full_name":"Ding, Yiming","last_name":"Ding"},{"full_name":"DiPasquale, Antonio","first_name":"Antonio","last_name":"DiPasquale"},{"full_name":"Dhokale, Bhausaheb","first_name":"Bhausaheb","last_name":"Dhokale"},{"last_name":"van Eijck","full_name":"van Eijck, Bouke P.","first_name":"Bouke P."},{"last_name":"Elsegood","full_name":"Elsegood, Mark R. 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In this first of two parts focusing on structure generation methods, many crystal structure prediction (CSP) methods performed well for the small but flexible agrochemical compound, successfully reproducing the experimentally observed crystal structures, while few groups were successful for the systems of higher complexity. A powder X-ray diffraction (PXRD) assisted exercise demonstrated the use of CSP in successfully determining a crystal structure from a low-quality PXRD pattern. The use of CSP in the prediction of likely cocrystal stoichiometry was also explored, demonstrating multiple possible approaches. Crystallographic disorder emerged as an important theme throughout the test as both a challenge for analysis and a major achievement where two groups blindly predicted the existence of disorder for the first time. Additionally, large-scale comparisons of the sets of predicted crystal structures also showed that some methods yield sets that largely contain the same crystal structures.","lang":"eng"}],"file_date_updated":"2025-01-29T11:09:48Z","title":"The seventh blind test of crystal structure prediction: Structure generation methods","doi":"10.1107/s2052520624007492","user_id":"317138e5-6ab7-11ef-aa6d-ffef3953e345","publisher":"International Union of Crystallography","status":"public","_id":"18952","type":"journal_article","language":[{"iso":"eng"}],"file":[{"content_type":"application/pdf","file_name":"2024_ActaCrystallographicaB_Hunnisett.pdf","access_level":"open_access","file_id":"18954","relation":"main_file","date_created":"2025-01-29T11:09:48Z","success":1,"creator":"dernst","file_size":10061037,"date_updated":"2025-01-29T11:09:48Z","checksum":"33b8083e76564cc918182b0b0b2cc023"}],"fulldoi":"https://doi.org/10.1107/s2052520624007492","isi":1,"external_id":{"isi":["001388840500003"],"pmid":["39405196"]},"day":"01","date_published":"2024-12-01T00:00:00Z","tmp":{"name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","image":"/images/cc_by.png","short":"CC BY (4.0)"},"supplementarymaterial":"yes"},{"year":"2024","oa":1,"article_processing_charge":"Yes (in subscription journal)","issue":"20","intvolume":"        20","oa_version":"Published Version","month":"10","author":[{"id":"cbe3cda4-d82c-11eb-8dc7-8ff94289fcc9","first_name":"Bingqing","orcid":"0000-0002-3584-9632","full_name":"Cheng, Bingqing","last_name":"Cheng"}],"article_type":"original","publication_status":"published","ddc":["540"],"dataavailabilitystatement":"The training scripts, trained models, and training data are available at https://github.com/BingqingCheng/cace-rm.","acknowledgement":"B.C. thanks Chris Pickard for enlightening discussions.","quality_controlled":"1","arxiv":1,"OA_place":"publisher","OA_type":"hybrid","publication_identifier":{"issn":["1549-9618"],"eissn":["1549-9626"]},"volume":20,"has_accepted_license":"1","related_material":{"link":[{"relation":"software","url":"https://github.com/BingqingCheng/cace"}]},"citation":{"ista":"Cheng B. 2024. Response matching for generating materials and molecules. Journal of Chemical Theory and Computation. 20(20), 9259–9266.","ama":"Cheng B. Response matching for generating materials and molecules. <i>Journal of Chemical Theory and Computation</i>. 2024;20(20):9259-9266. doi:<a href=\"https://doi.org/10.1021/acs.jctc.4c00998\">10.1021/acs.jctc.4c00998</a>","mla":"Cheng, Bingqing. “Response Matching for Generating Materials and Molecules.” <i>Journal of Chemical Theory and Computation</i>, vol. 20, no. 20, American Chemical Society, 2024, pp. 9259–66, doi:<a href=\"https://doi.org/10.1021/acs.jctc.4c00998\">10.1021/acs.jctc.4c00998</a>.","short":"B. Cheng, Journal of Chemical Theory and Computation 20 (2024) 9259–9266.","apa":"Cheng, B. (2024). Response matching for generating materials and molecules. <i>Journal of Chemical Theory and Computation</i>. American Chemical Society. <a href=\"https://doi.org/10.1021/acs.jctc.4c00998\">https://doi.org/10.1021/acs.jctc.4c00998</a>","ieee":"B. Cheng, “Response matching for generating materials and molecules,” <i>Journal of Chemical Theory and Computation</i>, vol. 20, no. 20. American Chemical Society, pp. 9259–9266, 2024.","chicago":"Cheng, Bingqing. “Response Matching for Generating Materials and Molecules.” <i>Journal of Chemical Theory and Computation</i>. American Chemical Society, 2024. <a href=\"https://doi.org/10.1021/acs.jctc.4c00998\">https://doi.org/10.1021/acs.jctc.4c00998</a>."},"corr_author":"1","publication":"Journal of Chemical Theory and Computation","researchdata_availability":"no","date_created":"2024-10-20T22:02:07Z","das_tickbox":"1","scopus_import":"1","department":[{"_id":"BiCh"}],"tmp":{"name":"Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)","short":"CC BY-NC-ND (4.0)","legal_code_url":"https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode","image":"/images/cc_by_nc_nd.png"},"supplementarymaterial":"no","language":[{"iso":"eng"}],"fulldoi":"https://doi.org/10.1021/acs.jctc.4c00998","isi":1,"file":[{"date_created":"2025-01-13T09:11:09Z","relation":"main_file","creator":"dernst","success":1,"file_size":4758251,"date_updated":"2025-01-13T09:11:09Z","checksum":"aca0011bba4846140809b5af583daa9a","content_type":"application/pdf","file_name":"2024_JCTC_Cheng.pdf","access_level":"open_access","file_id":"18832"}],"external_id":{"arxiv":["2405.09057"],"pmid":["39365029"],"isi":["001330001500001"]},"date_published":"2024-10-22T00:00:00Z","day":"22","doi":"10.1021/acs.jctc.4c00998","user_id":"317138e5-6ab7-11ef-aa6d-ffef3953e345","publisher":"American Chemical Society","_id":"18452","status":"public","type":"journal_article","page":"9259-9266","date_updated":"2026-09-24T11:30:58Z","pmid":1,"abstract":[{"text":"Diffusion models have recently emerged as powerful tools for the generation of new molecular and material structures. The key insight is that the noise in these models is related to the response of the atoms to displacement, and the denoising step is thus analogous to the geometry relaxation of atomistic systems starting from a random structure. Building on this, we present a generative method called Response Matching (RM), which leverages the fact that each stable material or molecule exists at the minimum of its potential energy surface. Any perturbation induces a response in energy and stress, driving the structure back to equilibrium. Matching this response is closely related to score matching in diffusion models. Another important aspect of state-of-the-art diffusion models is the incorporation of physical symmetries such as translation, rotation, and periodicity. RM employs a machine learning interatomic potential and random structure search as the denoising model, inherently respecting these symmetries and exploiting the locality of atomic interactions. RM handles both molecules and bulk materials under the same framework. Its efficiency and generalization are demonstrated on three systems: a small organic molecular data set, stable crystals from the Materials Project, and one-shot learning on a single diamond configuration.","lang":"eng"}],"file_date_updated":"2025-01-13T09:11:09Z","title":"Response matching for generating materials and molecules"},{"doi":"10.1038/s41524-024-01332-4","user_id":"317138e5-6ab7-11ef-aa6d-ffef3953e345","DOAJ_listed":"1","publisher":"Springer Nature","status":"public","_id":"17322","type":"journal_article","date_updated":"2026-09-24T11:30:25Z","abstract":[{"text":"Machine learning interatomic potentials are revolutionizing large-scale, accurate atomistic modeling in material science and chemistry. Many potentials use atomic cluster expansion or equivariant message-passing frameworks. Such frameworks typically use spherical harmonics as angular basis functions, followed by Clebsch-Gordan contraction to maintain rotational symmetry. We propose a mathematically equivalent and simple alternative that performs all operations in the Cartesian coordinates. This approach provides a complete set of polynormially independent features of atomic environments while maintaining interaction body orders. Additionally, we integrate low-dimensional embeddings of various chemical elements, trainable radial channel coupling, and inter-atomic message passing. The resulting potential, named Cartesian Atomic Cluster Expansion (CACE), exhibits good accuracy, stability, and generalizability. We validate its performance in diverse systems, including bulk water, small molecules, and 25-element high-entropy alloys.","lang":"eng"}],"file_date_updated":"2025-01-09T12:36:48Z","title":"Cartesian atomic cluster expansion for machine learning interatomic potentials","tmp":{"name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","image":"/images/cc_by.png","short":"CC BY (4.0)"},"supplementarymaterial":"no","language":[{"iso":"eng"}],"fulldoi":"https://doi.org/10.1038/s41524-024-01332-4","file":[{"relation":"main_file","date_created":"2025-01-09T12:36:48Z","creator":"dernst","success":1,"file_size":1659509,"checksum":"e6b4d1a45a9ef1e9be35b313d96ebd6f","date_updated":"2025-01-09T12:36:48Z","file_name":"2024_npjComputationalMaterials_Cheng.pdf","content_type":"application/pdf","access_level":"open_access","file_id":"18813"}],"isi":1,"external_id":{"arxiv":["2402.07472"],"isi":["001271730700001"]},"date_published":"2024-07-18T00:00:00Z","day":"18","article_number":"157","dataavailabilitystatement":"The water dataset is from https://github.com/BingqingCheng/ab-initio-thermodynamics-of-water. MD17-ethanol is from http://www.sgdml.org/#datasets. BPA is from ref. 43, downloaded from https://github.com/davkovacs/BOTNet-datasets. The HEA25 dataset is from ref. 45, downloaded from https://archive.materialscloud.org/record/2023.57. The training scripts, trained CACE potentials, and MD input files are available at https://github.com/BingqingCheng/cacefit.","acknowledgement":"B.C. thanks Ralf Drautz and Ngoc Cuong Nguyen for illuminating discussions.","arxiv":1,"OA_place":"publisher","quality_controlled":"1","OA_type":"gold","volume":10,"publication_identifier":{"eissn":["2057-3960"]},"has_accepted_license":"1","citation":{"ama":"Cheng B. Cartesian atomic cluster expansion for machine learning interatomic potentials. <i>npj Computational Materials</i>. 2024;10. doi:<a href=\"https://doi.org/10.1038/s41524-024-01332-4\">10.1038/s41524-024-01332-4</a>","mla":"Cheng, Bingqing. “Cartesian Atomic Cluster Expansion for Machine Learning Interatomic Potentials.” <i>Npj Computational Materials</i>, vol. 10, 157, Springer Nature, 2024, doi:<a href=\"https://doi.org/10.1038/s41524-024-01332-4\">10.1038/s41524-024-01332-4</a>.","ista":"Cheng B. 2024. Cartesian atomic cluster expansion for machine learning interatomic potentials. npj Computational Materials. 10, 157.","chicago":"Cheng, Bingqing. “Cartesian Atomic Cluster Expansion for Machine Learning Interatomic Potentials.” <i>Npj Computational Materials</i>. Springer Nature, 2024. <a href=\"https://doi.org/10.1038/s41524-024-01332-4\">https://doi.org/10.1038/s41524-024-01332-4</a>.","short":"B. Cheng, Npj Computational Materials 10 (2024).","ieee":"B. Cheng, “Cartesian atomic cluster expansion for machine learning interatomic potentials,” <i>npj Computational Materials</i>, vol. 10. Springer Nature, 2024.","apa":"Cheng, B. (2024). Cartesian atomic cluster expansion for machine learning interatomic potentials. <i>Npj Computational Materials</i>. Springer Nature. <a href=\"https://doi.org/10.1038/s41524-024-01332-4\">https://doi.org/10.1038/s41524-024-01332-4</a>"},"corr_author":"1","publication":"npj Computational Materials","researchdata_availability":"no","date_created":"2024-07-28T22:01:08Z","das_tickbox":"1","department":[{"_id":"BiCh"}],"scopus_import":"1","oa":1,"year":"2024","article_processing_charge":"Yes","oa_version":"Published Version","intvolume":"        10","month":"07","author":[{"last_name":"Cheng","orcid":"0000-0002-3584-9632","full_name":"Cheng, Bingqing","first_name":"Bingqing","id":"cbe3cda4-d82c-11eb-8dc7-8ff94289fcc9"}],"article_type":"original","publication_status":"published","ddc":["000"]},{"intvolume":"        14","oa_version":"Published Version","month":"02","oa":1,"year":"2023","article_processing_charge":"No","ddc":["540"],"author":[{"id":"cbe3cda4-d82c-11eb-8dc7-8ff94289fcc9","orcid":"0000-0002-3584-9632","full_name":"Cheng, Bingqing","first_name":"Bingqing","last_name":"Cheng"},{"last_name":"Hamel","first_name":"Sebastien","full_name":"Hamel, Sebastien"},{"last_name":"Bethkenhagen","orcid":"0000-0002-1838-2129","full_name":"Bethkenhagen, Mandy","first_name":"Mandy","id":"201939f4-803f-11ed-ab7e-d8da4bd1517f"}],"article_type":"original","publication_status":"published","quality_controlled":"1","publication_identifier":{"eissn":["2041-1723"]},"volume":14,"article_number":"1104","acknowledgement":"BC thanks Daan Frenkel for stimulating discussions. We thank Aleks Reinhardt, Daan Frenkel, Marius Millot, Federica Coppari, Rhys Bunting, and Chris J. Pickard for critically reading the manuscript and providing useful suggestions. BC acknowledges resources provided by the Cambridge Tier-2 system operated by the University of Cambridge Research Computing Service funded by EPSRC Tier-2 capital grant EP/P020259/1. SH acknowledges support from LDRD 19-ERD-031 and computing support from the Lawrence Livermore National Laboratory (LLNL) Institutional Computing Grand Challenge program. Lawrence Livermore National Laboratory is operated by Lawrence Livermore National Security, LLC, for the U.S. Department of Energy, National Nuclear Security Administration under Contract DE-AC52-07NA27344. MB acknowledges support by the European Horizon 2020 program within the Marie Skłodowska-Curie actions (xICE grant number 894725), funding from the NOMIS foundation and computational resources at the North-German Supercomputing Alliance (HLRN) facilities.","dataavailabilitystatement":"All original data generated for the study, including the MLP, the training set, simulation input files, intermediate data, PYTHON notebook, are in the SI repository https://github.com/BingqingCheng/highp-ch\r\nhttps://doi.org/10.5281/ZENODO.7578498","publication":"Nature Communications","researchdata_availability":"yes","date_created":"2023-03-05T23:01:04Z","das_tickbox":"1","scopus_import":"1","department":[{"_id":"BiCh"}],"has_accepted_license":"1","citation":{"ista":"Cheng B, Hamel S, Bethkenhagen M. 2023. Thermodynamics of diamond formation from hydrocarbon mixtures in planets. Nature Communications. 14, 1104.","ama":"Cheng B, Hamel S, Bethkenhagen M. Thermodynamics of diamond formation from hydrocarbon mixtures in planets. <i>Nature Communications</i>. 2023;14. doi:<a href=\"https://doi.org/10.1038/s41467-023-36841-1\">10.1038/s41467-023-36841-1</a>","mla":"Cheng, Bingqing, et al. “Thermodynamics of Diamond Formation from Hydrocarbon Mixtures in Planets.” <i>Nature Communications</i>, vol. 14, 1104, Springer Nature, 2023, doi:<a href=\"https://doi.org/10.1038/s41467-023-36841-1\">10.1038/s41467-023-36841-1</a>.","apa":"Cheng, B., Hamel, S., &#38; Bethkenhagen, M. (2023). Thermodynamics of diamond formation from hydrocarbon mixtures in planets. <i>Nature Communications</i>. Springer Nature. <a href=\"https://doi.org/10.1038/s41467-023-36841-1\">https://doi.org/10.1038/s41467-023-36841-1</a>","ieee":"B. Cheng, S. Hamel, and M. Bethkenhagen, “Thermodynamics of diamond formation from hydrocarbon mixtures in planets,” <i>Nature Communications</i>, vol. 14. Springer Nature, 2023.","short":"B. Cheng, S. Hamel, M. Bethkenhagen, Nature Communications 14 (2023).","chicago":"Cheng, Bingqing, Sebastien Hamel, and Mandy Bethkenhagen. “Thermodynamics of Diamond Formation from Hydrocarbon Mixtures in Planets.” <i>Nature Communications</i>. Springer Nature, 2023. <a href=\"https://doi.org/10.1038/s41467-023-36841-1\">https://doi.org/10.1038/s41467-023-36841-1</a>."},"corr_author":"1","supplementarymaterial":"no","tmp":{"name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","image":"/images/cc_by.png","short":"CC BY (4.0)"},"day":"27","date_published":"2023-02-27T00:00:00Z","language":[{"iso":"eng"}],"file":[{"checksum":"5ff61ad21511950c15abb73b18613883","date_updated":"2023-03-07T10:58:00Z","file_size":1946443,"creator":"cchlebak","success":1,"relation":"main_file","date_created":"2023-03-07T10:58:00Z","file_id":"12713","access_level":"open_access","content_type":"application/pdf","file_name":"2023_NatComm_Cheng.pdf"}],"fulldoi":"https://doi.org/10.1038/s41467-023-36841-1","isi":1,"external_id":{"isi":["000939678300002"],"pmid":["36843123"]},"status":"public","_id":"12702","project":[{"_id":"9B861AAC-BA93-11EA-9121-9846C619BF3A","name":"NOMIS Fellowship Program"}],"type":"journal_article","doi":"10.1038/s41467-023-36841-1","user_id":"317138e5-6ab7-11ef-aa6d-ffef3953e345","publisher":"Springer Nature","abstract":[{"text":"Hydrocarbon mixtures are extremely abundant in the Universe, and diamond formation from them can play a crucial role in shaping the interior structure and evolution of planets. With first-principles accuracy, we first estimate the melting line of diamond, and then reveal the nature of chemical bonding in hydrocarbons at extreme conditions. We finally establish the pressure-temperature phase boundary where it is thermodynamically possible for diamond to form from hydrocarbon mixtures with different atomic fractions of carbon. Notably, here we show a depletion zone at pressures above 200 GPa and temperatures below 3000 K-3500 K where diamond formation is thermodynamically favorable regardless of the carbon atomic fraction, due to a phase separation mechanism. The cooler condition of the interior of Neptune compared to Uranus means that the former is much more likely to contain the depletion zone. Our findings can help explain the dichotomy of the two ice giants manifested by the low luminosity of Uranus, and lead to a better understanding of (exo-)planetary formation and evolution.","lang":"eng"}],"file_date_updated":"2023-03-07T10:58:00Z","title":"Thermodynamics of diamond formation from hydrocarbon mixtures in planets","date_updated":"2026-08-07T10:50:07Z","pmid":1},{"intvolume":"       158","issue":"16","oa_version":"Published Version","month":"04","article_processing_charge":"No","oa":1,"year":"2023","ddc":["540"],"article_type":"original","publication_status":"published","author":[{"last_name":"Schmid","full_name":"Schmid, Rochus","first_name":"Rochus"},{"id":"cbe3cda4-d82c-11eb-8dc7-8ff94289fcc9","full_name":"Cheng, Bingqing","orcid":"0000-0002-3584-9632","first_name":"Bingqing","last_name":"Cheng"}],"quality_controlled":"1","arxiv":1,"volume":158,"publication_identifier":{"eissn":["1089-7690"]},"acknowledgement":"We thank Aleks Reinhardt and Daan Frenkel for their insightful comments and suggestions on the article. B.C. acknowledges the resources provided by the Cambridge Tier-2 system operated by the University of Cambridge Research Computing Service funded by EPSRC Tier-2 capital Grant No. EP/P020259/1.","dataavailabilitystatement":"Additional simulation details are provided in the supplementary material. All Python scripts and simulation input files generated for the study are in the supplementary material repository at https://github.com/BingqingCheng/mu-adsorption. Scripts for the S0 analysis are at https://github.com/BingqingCheng/S0.","article_number":"161101 ","researchdata_availability":"yes","publication":"The Journal of Chemical Physics","scopus_import":"1","department":[{"_id":"BiCh"}],"das_tickbox":"1","date_created":"2023-05-07T22:01:03Z","related_material":{"link":[{"url":"https://github.com/BingqingCheng/mu-adsorption","relation":"software"},{"url":"https://github.com/BingqingCheng/S0","relation":"software"}]},"has_accepted_license":"1","corr_author":"1","citation":{"mla":"Schmid, Rochus, and Bingqing Cheng. “Computing Chemical Potentials of Adsorbed or Confined Fluids.” <i>The Journal of Chemical Physics</i>, vol. 158, no. 16, 161101, AIP Publishing, 2023, doi:<a href=\"https://doi.org/10.1063/5.0146711\">10.1063/5.0146711</a>.","ama":"Schmid R, Cheng B. Computing chemical potentials of adsorbed or confined fluids. <i>The Journal of Chemical Physics</i>. 2023;158(16). doi:<a href=\"https://doi.org/10.1063/5.0146711\">10.1063/5.0146711</a>","ista":"Schmid R, Cheng B. 2023. Computing chemical potentials of adsorbed or confined fluids. The Journal of Chemical Physics. 158(16), 161101.","chicago":"Schmid, Rochus, and Bingqing Cheng. “Computing Chemical Potentials of Adsorbed or Confined Fluids.” <i>The Journal of Chemical Physics</i>. AIP Publishing, 2023. <a href=\"https://doi.org/10.1063/5.0146711\">https://doi.org/10.1063/5.0146711</a>.","short":"R. Schmid, B. Cheng, The Journal of Chemical Physics 158 (2023).","ieee":"R. Schmid and B. Cheng, “Computing chemical potentials of adsorbed or confined fluids,” <i>The Journal of Chemical Physics</i>, vol. 158, no. 16. AIP Publishing, 2023.","apa":"Schmid, R., &#38; Cheng, B. (2023). Computing chemical potentials of adsorbed or confined fluids. <i>The Journal of Chemical Physics</i>. AIP Publishing. <a href=\"https://doi.org/10.1063/5.0146711\">https://doi.org/10.1063/5.0146711</a>"},"supplementarymaterial":"yes","tmp":{"name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","image":"/images/cc_by.png","short":"CC BY (4.0)"},"date_published":"2023-04-24T00:00:00Z","day":"24","fulldoi":"https://doi.org/10.1063/5.0146711","isi":1,"file":[{"file_name":"2023_JourChemicalPhysics_Schmid.pdf","content_type":"application/pdf","access_level":"open_access","file_id":"12918","relation":"main_file","date_created":"2023-05-08T07:44:49Z","creator":"dernst","success":1,"file_size":6499468,"date_updated":"2023-05-08T07:44:49Z","checksum":"4ab8c965f2fa4e17920bfa846847f137"}],"language":[{"iso":"eng"}],"external_id":{"arxiv":["2302.01297"],"isi":["001010676000010"],"pmid":["37093149"]},"type":"journal_article","status":"public","_id":"12912","doi":"10.1063/5.0146711","publisher":"AIP Publishing","user_id":"317138e5-6ab7-11ef-aa6d-ffef3953e345","file_date_updated":"2023-05-08T07:44:49Z","abstract":[{"lang":"eng","text":"The chemical potential of adsorbed or confined fluids provides insight into their unique thermodynamic properties and determines adsorption isotherms. However, it is often difficult to compute this quantity from atomistic simulations using existing statistical mechanical methods. We introduce a computational framework that utilizes static structure factors, thermodynamic integration, and free energy perturbation for calculating the absolute chemical potential of fluids. For demonstration, we apply the method to compute the adsorption isotherms of carbon dioxide in a metal-organic framework and water in carbon nanotubes."}],"title":"Computing chemical potentials of adsorbed or confined fluids","date_updated":"2026-08-07T10:55:19Z","pmid":1},{"article_type":"original","publication_status":"published","author":[{"last_name":"Chen","id":"c636c5ca-e8b8-11ed-b2d4-cc2c37613a8d","first_name":"Ke","full_name":"Chen, Ke"},{"last_name":"Kunkel","full_name":"Kunkel, Christian","first_name":"Christian"},{"last_name":"Cheng","full_name":"Cheng, Bingqing","orcid":"0000-0002-3584-9632","first_name":"Bingqing","id":"cbe3cda4-d82c-11eb-8dc7-8ff94289fcc9"},{"last_name":"Reuter","full_name":"Reuter, Karsten","first_name":"Karsten"},{"first_name":"Johannes T.","full_name":"Margraf, Johannes T.","last_name":"Margraf"}],"ddc":["000","540"],"article_processing_charge":"No","year":"2023","oa":1,"oa_version":"Published Version","month":"04","has_accepted_license":"1","citation":{"ieee":"K. Chen, C. Kunkel, B. Cheng, K. Reuter, and J. T. Margraf, “Physics-inspired machine learning of localized intensive properties,” <i>Chemical Science</i>. Royal Society of Chemistry, 2023.","apa":"Chen, K., Kunkel, C., Cheng, B., Reuter, K., &#38; Margraf, J. T. (2023). Physics-inspired machine learning of localized intensive properties. <i>Chemical Science</i>. Royal Society of Chemistry. <a href=\"https://doi.org/10.1039/d3sc00841j\">https://doi.org/10.1039/d3sc00841j</a>","short":"K. Chen, C. Kunkel, B. Cheng, K. Reuter, J.T. Margraf, Chemical Science (2023).","chicago":"Chen, Ke, Christian Kunkel, Bingqing Cheng, Karsten Reuter, and Johannes T. Margraf. “Physics-Inspired Machine Learning of Localized Intensive Properties.” <i>Chemical Science</i>. Royal Society of Chemistry, 2023. <a href=\"https://doi.org/10.1039/d3sc00841j\">https://doi.org/10.1039/d3sc00841j</a>.","ista":"Chen K, Kunkel C, Cheng B, Reuter K, Margraf JT. 2023. Physics-inspired machine learning of localized intensive properties. Chemical Science.","ama":"Chen K, Kunkel C, Cheng B, Reuter K, Margraf JT. Physics-inspired machine learning of localized intensive properties. <i>Chemical Science</i>. 2023. doi:<a href=\"https://doi.org/10.1039/d3sc00841j\">10.1039/d3sc00841j</a>","mla":"Chen, Ke, et al. “Physics-Inspired Machine Learning of Localized Intensive Properties.” <i>Chemical Science</i>, Royal Society of Chemistry, 2023, doi:<a href=\"https://doi.org/10.1039/d3sc00841j\">10.1039/d3sc00841j</a>."},"researchdata_availability":"yes","publication":"Chemical Science","scopus_import":"1","department":[{"_id":"BiCh"}],"date_created":"2023-04-30T22:01:06Z","das_tickbox":"1","acknowledgement":"KC acknowledges funding from the China Scholarship Council. KC is grateful for the TUM graduate school finance support to visit Bingqing Cheng's group in IST for two months. We also thankfully acknowledge computational resources provided by the MPCDF Supercomputing Centre.","dataavailabilitystatement":"Data and code for this paper are publicly available at https://gitlab.mpcdf.mpg.de/kchen/localized-intensive-property-prediciton.git.","quality_controlled":"1","publication_identifier":{"eissn":["2041-6539"],"issn":["2041-6520"]},"file":[{"date_created":"2023-05-02T07:17:05Z","relation":"main_file","creator":"dernst","success":1,"file_size":1515446,"checksum":"5eeec69a51e192dcd94b955d84423836","date_updated":"2023-05-02T07:17:05Z","file_name":"2023_ChemialScience_Chen.pdf","content_type":"application/pdf","access_level":"open_access","file_id":"12883"}],"fulldoi":"https://doi.org/10.1039/d3sc00841j","isi":1,"language":[{"iso":"eng"}],"external_id":{"isi":["000971508100001"]},"day":"10","date_published":"2023-04-10T00:00:00Z","tmp":{"short":"CC BY (3.0)","image":"/images/cc_by.png","legal_code_url":"https://creativecommons.org/licenses/by/3.0/legalcode","name":"Creative Commons Attribution 3.0 Unported (CC BY 3.0)"},"supplementarymaterial":"yes","date_updated":"2026-08-07T10:53:37Z","license":"https://creativecommons.org/licenses/by/3.0/","file_date_updated":"2023-05-02T07:17:05Z","abstract":[{"text":"Machine learning (ML) has been widely applied to chemical property prediction, most prominently for the energies and forces in molecules and materials. The strong interest in predicting energies in particular has led to a ‘local energy’-based paradigm for modern atomistic ML models, which ensures size-extensivity and a linear scaling of computational cost with system size. However, many electronic properties (such as excitation energies or ionization energies) do not necessarily scale linearly with system size and may even be spatially localized. Using size-extensive models in these cases can lead to large errors. In this work, we explore different strategies for learning intensive and localized properties, using HOMO energies in organic molecules as a representative test case. In particular, we analyze the pooling functions that atomistic neural networks use to predict molecular properties, and suggest an orbital weighted average (OWA) approach that enables the accurate prediction of orbital energies and locations.","lang":"eng"}],"title":"Physics-inspired machine learning of localized intensive properties","doi":"10.1039/d3sc00841j","publisher":"Royal Society of Chemistry","user_id":"317138e5-6ab7-11ef-aa6d-ffef3953e345","type":"journal_article","_id":"12879","status":"public"},{"ddc":["540","000"],"author":[{"full_name":"Zeng, Zezhu","orcid":"0000-0001-5126-4928","first_name":"Zezhu","id":"54a2c730-803f-11ed-ab7e-95b29d2680e7","last_name":"Zeng"},{"last_name":"Wodaczek","id":"8b4b6a9f-32b0-11ee-9fa8-bbe85e26258e","orcid":"0009-0000-1457-795X","full_name":"Wodaczek, Felix","first_name":"Felix"},{"last_name":"Liu","first_name":"Keyang","full_name":"Liu, Keyang"},{"first_name":"Frederick","full_name":"Stein, Frederick","last_name":"Stein"},{"last_name":"Hutter","full_name":"Hutter, Jürg","first_name":"Jürg"},{"last_name":"Chen","first_name":"Ji","full_name":"Chen, Ji"},{"last_name":"Cheng","orcid":"0000-0002-3584-9632","full_name":"Cheng, Bingqing","first_name":"Bingqing","id":"cbe3cda4-d82c-11eb-8dc7-8ff94289fcc9"}],"article_type":"original","publication_status":"published","month":"10","oa_version":"Published Version","intvolume":"        14","oa":1,"year":"2023","article_processing_charge":"Yes","date_created":"2023-10-15T22:01:10Z","das_tickbox":"1","department":[{"_id":"BiCh"},{"_id":"GradSch"}],"scopus_import":"1","publication":"Nature Communications","researchdata_availability":"yes","citation":{"ama":"Zeng Z, Wodaczek F, Liu K, et al. Mechanistic insight on water dissociation on pristine low-index TiO2 surfaces from machine learning molecular dynamics simulations. <i>Nature Communications</i>. 2023;14. doi:<a href=\"https://doi.org/10.1038/s41467-023-41865-8\">10.1038/s41467-023-41865-8</a>","mla":"Zeng, Zezhu, et al. “Mechanistic Insight on Water Dissociation on Pristine Low-Index TiO2 Surfaces from Machine Learning Molecular Dynamics Simulations.” <i>Nature Communications</i>, vol. 14, 6131, Springer Nature, 2023, doi:<a href=\"https://doi.org/10.1038/s41467-023-41865-8\">10.1038/s41467-023-41865-8</a>.","ista":"Zeng Z, Wodaczek F, Liu K, Stein F, Hutter J, Chen J, Cheng B. 2023. Mechanistic insight on water dissociation on pristine low-index TiO2 surfaces from machine learning molecular dynamics simulations. Nature Communications. 14, 6131.","chicago":"Zeng, Zezhu, Felix Wodaczek, Keyang Liu, Frederick Stein, Jürg Hutter, Ji Chen, and Bingqing Cheng. “Mechanistic Insight on Water Dissociation on Pristine Low-Index TiO2 Surfaces from Machine Learning Molecular Dynamics Simulations.” <i>Nature Communications</i>. Springer Nature, 2023. <a href=\"https://doi.org/10.1038/s41467-023-41865-8\">https://doi.org/10.1038/s41467-023-41865-8</a>.","apa":"Zeng, Z., Wodaczek, F., Liu, K., Stein, F., Hutter, J., Chen, J., &#38; Cheng, B. (2023). Mechanistic insight on water dissociation on pristine low-index TiO2 surfaces from machine learning molecular dynamics simulations. <i>Nature Communications</i>. Springer Nature. <a href=\"https://doi.org/10.1038/s41467-023-41865-8\">https://doi.org/10.1038/s41467-023-41865-8</a>","ieee":"Z. Zeng <i>et al.</i>, “Mechanistic insight on water dissociation on pristine low-index TiO2 surfaces from machine learning molecular dynamics simulations,” <i>Nature Communications</i>, vol. 14. Springer Nature, 2023.","short":"Z. Zeng, F. Wodaczek, K. Liu, F. Stein, J. Hutter, J. Chen, B. Cheng, Nature Communications 14 (2023)."},"corr_author":"1","has_accepted_license":"1","related_material":{"link":[{"url":"https://github.com/BingqingCheng/TiO2-water","relation":"software"}]},"publication_identifier":{"eissn":["2041-1723"]},"ec_funded":1,"volume":14,"quality_controlled":"1","arxiv":1,"article_number":"6131","acknowledgement":"F.S., J.H., and B.C. thank the Swiss National Supercomputing Centre (CSCS) for the generous allocation of CPU hours via production project s1108 at the Piz Daint supercomputer. B.C. acknowledges resources provided by the Cambridge Tier-2 system operated by the University of Cambridge Research Computing Service funded by EPSRC Tier-2 capital grant EP/P020259/1. J.C. acknowledges the Beijing Natural Science Foundation for support under grant No. JQ22001. F.S., and J.H. thank the Swiss Platform for Advanced Scientific Computing (PASC) via the 2021-2024 “Ab Initio Molecular Dynamics at the Exa-Scale” project. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 101034413.","dataavailabilitystatement":"The machine learning potentials, training sets, sample DFT and metadynamics input files, PYTHON data analysis scripts and other necessary source data files generated for this study are available in the SI repository (https://github.com/BingqingCheng/TiO2-water) see ref 58.\r\n58. Zeng, Z. et al. Source data for Mechanistic insight on water dissociation on pristine low-index TiO2 surfaces from machine learning molecular dynamics simulations, Zenodo, https://zenodo.org/record/8301965 (2023).","day":"02","date_published":"2023-10-02T00:00:00Z","external_id":{"arxiv":["2303.07433"],"isi":["001084354900008"],"pmid":["37783698"]},"language":[{"iso":"eng"}],"fulldoi":"https://doi.org/10.1038/s41467-023-41865-8","isi":1,"file":[{"checksum":"7d1dffd36b672ec679f08f70ce79da87","date_updated":"2023-10-16T07:34:49Z","file_size":3194116,"success":1,"creator":"dernst","relation":"main_file","date_created":"2023-10-16T07:34:49Z","file_id":"14432","access_level":"open_access","file_name":"2023_NatureComm_Zeng.pdf","content_type":"application/pdf"}],"supplementarymaterial":"yes","tmp":{"name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","image":"/images/cc_by.png","short":"CC BY (4.0)"},"title":"Mechanistic insight on water dissociation on pristine low-index TiO2 surfaces from machine learning molecular dynamics simulations","abstract":[{"text":"Water adsorption and dissociation processes on pristine low-index TiO2 interfaces are important but poorly understood outside the well-studied anatase (101) and rutile (110). To understand these, we construct three sets of machine learning potentials that are simultaneously applicable to various TiO2 surfaces, based on three density-functional-theory approximations. Here we show the water dissociation free energies on seven pristine TiO2 surfaces, and predict that anatase (100), anatase (110), rutile (001), and rutile (011) favor water dissociation, anatase (101) and rutile (100) have mostly molecular adsorption, while the simulations of rutile (110) sensitively depend on the slab thickness and molecular adsorption is preferred with thick slabs. Moreover, using an automated algorithm, we reveal that these surfaces follow different types of atomistic mechanisms for proton transfer and water dissociation: one-step, two-step, or both. These mechanisms can be rationalized based on the arrangements of water molecules on the different surfaces. Our finding thus demonstrates that the different pristine TiO2 surfaces react with water in distinct ways, and cannot be represented using just the low-energy anatase (101) and rutile (110) surfaces.","lang":"eng"}],"file_date_updated":"2023-10-16T07:34:49Z","pmid":1,"date_updated":"2026-08-07T11:00:29Z","status":"public","_id":"14425","type":"journal_article","project":[{"grant_number":"101034413","_id":"fc2ed2f7-9c52-11eb-aca3-c01059dda49c","name":"IST-BRIDGE: International postdoctoral program","call_identifier":"H2020"}],"user_id":"317138e5-6ab7-11ef-aa6d-ffef3953e345","publisher":"Springer Nature","doi":"10.1038/s41467-023-41865-8"},{"related_material":{"record":[{"relation":"research_data","status":"public","id":"14619"}]},"has_accepted_license":"1","corr_author":"1","citation":{"chicago":"Reinhardt, Aleks, Pin Yu Chew, and Bingqing Cheng. “A Streamlined Molecular-Dynamics Workflow for Computing Solubilities of Molecular and Ionic Crystals.” <i>Journal of Chemical Physics</i>. AIP Publishing, 2023. <a href=\"https://doi.org/10.1063/5.0173341\">https://doi.org/10.1063/5.0173341</a>.","short":"A. Reinhardt, P.Y. Chew, B. Cheng, Journal of Chemical Physics 159 (2023).","ieee":"A. Reinhardt, P. Y. Chew, and B. Cheng, “A streamlined molecular-dynamics workflow for computing solubilities of molecular and ionic crystals,” <i>Journal of Chemical Physics</i>, vol. 159, no. 18. AIP Publishing, 2023.","apa":"Reinhardt, A., Chew, P. Y., &#38; Cheng, B. (2023). A streamlined molecular-dynamics workflow for computing solubilities of molecular and ionic crystals. <i>Journal of Chemical Physics</i>. AIP Publishing. <a href=\"https://doi.org/10.1063/5.0173341\">https://doi.org/10.1063/5.0173341</a>","mla":"Reinhardt, Aleks, et al. “A Streamlined Molecular-Dynamics Workflow for Computing Solubilities of Molecular and Ionic Crystals.” <i>Journal of Chemical Physics</i>, vol. 159, no. 18, 184110, AIP Publishing, 2023, doi:<a href=\"https://doi.org/10.1063/5.0173341\">10.1063/5.0173341</a>.","ama":"Reinhardt A, Chew PY, Cheng B. A streamlined molecular-dynamics workflow for computing solubilities of molecular and ionic crystals. <i>Journal of Chemical Physics</i>. 2023;159(18). doi:<a href=\"https://doi.org/10.1063/5.0173341\">10.1063/5.0173341</a>","ista":"Reinhardt A, Chew PY, Cheng B. 2023. A streamlined molecular-dynamics workflow for computing solubilities of molecular and ionic crystals. Journal of Chemical Physics. 159(18), 184110."},"researchdata_availability":"yes","publication":"Journal of Chemical Physics","scopus_import":"1","department":[{"_id":"BiCh"}],"das_tickbox":"1","date_created":"2023-11-26T23:00:54Z","dataavailabilitystatement":"Simulation input files necessary to reproduce the study and Python data analysis scripts are available in the SI repository at https://github.com/BingqingCheng/solubility, and on Zenodo at http://doi.org/10.5281/zenodo.8398093.","acknowledgement":"A.R. and B.C. acknowledge resources provided by the Cambridge Tier-2 system operated by the University of Cambridge Research Computing Service funded by EPSRC Tier-2 capital Grant No. EP/P020259/1. P.Y.C. acknowledges support from the Ernest Oppenheimer Fund and the Winton Programme for the Physics of Sustainability.","article_number":"184110","quality_controlled":"1","arxiv":1,"volume":159,"publication_identifier":{"eissn":["1089-7690"],"issn":["0021-9606"]},"article_type":"original","publication_status":"published","author":[{"last_name":"Reinhardt","first_name":"Aleks","full_name":"Reinhardt, Aleks"},{"first_name":"Pin Yu","full_name":"Chew, Pin Yu","last_name":"Chew"},{"id":"cbe3cda4-d82c-11eb-8dc7-8ff94289fcc9","first_name":"Bingqing","orcid":"0000-0002-3584-9632","full_name":"Cheng, Bingqing","last_name":"Cheng"}],"ddc":["530","540"],"article_processing_charge":"Yes (in subscription journal)","oa":1,"year":"2023","issue":"18","oa_version":"Published Version","intvolume":"       159","month":"11","date_updated":"2026-08-07T11:07:46Z","pmid":1,"file_date_updated":"2023-11-28T08:39:06Z","abstract":[{"lang":"eng","text":"Computing the solubility of crystals in a solvent using atomistic simulations is notoriously challenging due to the complexities and convergence issues associated with free-energy methods, as well as the slow equilibration in direct-coexistence simulations. This paper introduces a molecular-dynamics workflow that simplifies and robustly computes the solubility of molecular or ionic crystals. This method is considerably more straightforward than the state-of-the-art, as we have streamlined and optimised each step of the process. Specifically, we calculate the chemical potential of the crystal using the gas-phase molecule as a reference state, and employ the S0 method to determine the concentration dependence of the chemical potential of the solute. We use this workflow to predict the solubilities of sodium chloride in water, urea polymorphs in water, and paracetamol polymorphs in both water and ethanol. Our findings indicate that the predicted solubility is sensitive to the chosen potential energy surface. Furthermore, we note that the harmonic approximation often fails for both molecular crystals and gas molecules at or above room temperature, and that the assumption of an ideal solution becomes less valid for highly soluble substances."}],"title":"A streamlined molecular-dynamics workflow for computing solubilities of molecular and ionic crystals","doi":"10.1063/5.0173341","publisher":"AIP Publishing","user_id":"317138e5-6ab7-11ef-aa6d-ffef3953e345","type":"journal_article","_id":"14603","status":"public","file":[{"access_level":"open_access","file_id":"14620","content_type":"application/pdf","file_name":"2023_JourChemicalPhysics_Reinhardt.pdf","file_size":6276059,"checksum":"f668ee0d07096eef81159d05bc27aabc","date_updated":"2023-11-28T08:39:06Z","relation":"main_file","date_created":"2023-11-28T08:39:06Z","success":1,"creator":"dernst"}],"fulldoi":"https://doi.org/10.1063/5.0173341","isi":1,"language":[{"iso":"eng"}],"external_id":{"pmid":["37962445"],"isi":["001137066700001"],"arxiv":["2308.10886"]},"day":"14","date_published":"2023-11-14T00:00:00Z","tmp":{"name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","image":"/images/cc_by.png","short":"CC BY (4.0)"},"supplementarymaterial":"no"},{"related_material":{"record":[{"id":"14603","status":"public","relation":"used_in_publication"}]},"date_updated":"2026-08-07T11:07:45Z","has_accepted_license":"1","corr_author":"1","citation":{"chicago":"Cheng, Bingqing. “BingqingCheng/Solubility: V1.0.” Zenodo, 2023. <a href=\"https://doi.org/10.5281/ZENODO.8398094\">https://doi.org/10.5281/ZENODO.8398094</a>.","short":"B. Cheng, (2023).","apa":"Cheng, B. (2023). BingqingCheng/solubility: V1.0. Zenodo. <a href=\"https://doi.org/10.5281/ZENODO.8398094\">https://doi.org/10.5281/ZENODO.8398094</a>","ieee":"B. Cheng, “BingqingCheng/solubility: V1.0.” Zenodo, 2023.","mla":"Cheng, Bingqing. <i>BingqingCheng/Solubility: V1.0</i>. Zenodo, 2023, doi:<a href=\"https://doi.org/10.5281/ZENODO.8398094\">10.5281/ZENODO.8398094</a>.","ama":"Cheng B. BingqingCheng/solubility: V1.0. 2023. doi:<a href=\"https://doi.org/10.5281/ZENODO.8398094\">10.5281/ZENODO.8398094</a>","ista":"Cheng B. 2023. BingqingCheng/solubility: V1.0, Zenodo, <a href=\"https://doi.org/10.5281/ZENODO.8398094\">10.5281/ZENODO.8398094</a>."},"abstract":[{"text":"Data underlying the publication \"A streamlined molecular-dynamics workflow for computing solubilities of molecular and ionic crystals\" (DOI https://doi.org/10.1063/5.0173341).","lang":"eng"}],"department":[{"_id":"BiCh"}],"date_created":"2023-11-28T08:32:18Z","title":"BingqingCheng/solubility: V1.0","doi":"10.5281/ZENODO.8398094","publisher":"Zenodo","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","type":"research_data_reference","_id":"14619","status":"public","fulldoi":"https://doi.org/10.5281/ZENODO.8398094","author":[{"id":"cbe3cda4-d82c-11eb-8dc7-8ff94289fcc9","full_name":"Cheng, Bingqing","orcid":"0000-0002-3584-9632","first_name":"Bingqing","last_name":"Cheng"}],"date_published":"2023-10-02T00:00:00Z","day":"02","ddc":["530"],"article_processing_charge":"No","oa":1,"year":"2023","main_file_link":[{"open_access":"1","url":"https://doi.org/10.5281/zenodo.8398094"}],"oa_version":"Published Version","month":"10"}]