[{"acknowledgement":"This research was carried out with the support of the Russian Foundation for Basic Research(grant no. 19-01-00169)","external_id":{"isi":["000625983100001"],"arxiv":["1511.03501"]},"quality_controlled":"1","publication_status":"published","publisher":"IOP Publishing","volume":75,"publication":"Russian Mathematical Surveys","publication_identifier":{"issn":["0036-0279"]},"arxiv":1,"status":"public","day":"01","main_file_link":[{"url":"https://arxiv.org/abs/1511.03501","open_access":"1"}],"month":"12","doi":"10.1070/RM9943","article_processing_charge":"No","year":"2020","article_type":"original","related_material":{"record":[{"id":"10220","status":"public","relation":"later_version"},{"relation":"earlier_version","id":"8183","status":"public"}]},"intvolume":" 75","author":[{"id":"3827DAC8-F248-11E8-B48F-1D18A9856A87","full_name":"Avvakumov, Sergey","last_name":"Avvakumov","orcid":"0000-0002-7840-5062","first_name":"Sergey"},{"id":"36690CA2-F248-11E8-B48F-1D18A9856A87","full_name":"Wagner, Uli","last_name":"Wagner","orcid":"0000-0002-1494-0568","first_name":"Uli"},{"id":"32BF9DAA-F248-11E8-B48F-1D18A9856A87","full_name":"Mabillard, Isaac","last_name":"Mabillard","first_name":"Isaac"},{"full_name":"Skopenkov, A. B.","last_name":"Skopenkov","first_name":"A. B."}],"scopus_import":"1","title":"Eliminating higher-multiplicity intersections, III. Codimension 2","user_id":"4359f0d1-fa6c-11eb-b949-802e58b17ae8","type":"journal_article","oa_version":"Preprint","date_created":"2021-04-04T22:01:22Z","isi":1,"oa":1,"issue":"6","language":[{"iso":"eng"}],"date_updated":"2025-07-02T10:54:51Z","_id":"9308","citation":{"ista":"Avvakumov S, Wagner U, Mabillard I, Skopenkov AB. 2020. Eliminating higher-multiplicity intersections, III. Codimension 2. Russian Mathematical Surveys. 75(6), 1156–1158.","chicago":"Avvakumov, Sergey, Uli Wagner, Isaac Mabillard, and A. B. Skopenkov. “Eliminating Higher-Multiplicity Intersections, III. Codimension 2.” Russian Mathematical Surveys. IOP Publishing, 2020. https://doi.org/10.1070/RM9943.","ama":"Avvakumov S, Wagner U, Mabillard I, Skopenkov AB. Eliminating higher-multiplicity intersections, III. Codimension 2. Russian Mathematical Surveys. 2020;75(6):1156-1158. doi:10.1070/RM9943","ieee":"S. Avvakumov, U. Wagner, I. Mabillard, and A. B. Skopenkov, “Eliminating higher-multiplicity intersections, III. Codimension 2,” Russian Mathematical Surveys, vol. 75, no. 6. IOP Publishing, pp. 1156–1158, 2020.","mla":"Avvakumov, Sergey, et al. “Eliminating Higher-Multiplicity Intersections, III. Codimension 2.” Russian Mathematical Surveys, vol. 75, no. 6, IOP Publishing, 2020, pp. 1156–58, doi:10.1070/RM9943.","short":"S. Avvakumov, U. Wagner, I. Mabillard, A.B. Skopenkov, Russian Mathematical Surveys 75 (2020) 1156–1158.","apa":"Avvakumov, S., Wagner, U., Mabillard, I., & Skopenkov, A. B. (2020). Eliminating higher-multiplicity intersections, III. Codimension 2. Russian Mathematical Surveys. IOP Publishing. https://doi.org/10.1070/RM9943"},"date_published":"2020-12-01T00:00:00Z","department":[{"_id":"UlWa"}],"page":"1156-1158"},{"author":[{"last_name":"Gupta","first_name":"Chitrak","full_name":"Gupta, Chitrak"},{"full_name":"Khaniya, Umesh","first_name":"Umesh","last_name":"Khaniya"},{"first_name":"Chun","last_name":"Chan","full_name":"Chan, Chun"},{"full_name":"Dehez, Francois","last_name":"Dehez","first_name":"Francois"},{"last_name":"Shekhar","first_name":"Mrinal","full_name":"Shekhar, Mrinal"},{"full_name":"Gunner, M. R.","last_name":"Gunner","first_name":"M. R."},{"id":"338D39FE-F248-11E8-B48F-1D18A9856A87","full_name":"Sazanov, Leonid A","last_name":"Sazanov","orcid":"0000-0002-0977-7989","first_name":"Leonid A"},{"full_name":"Chipot, Christophe","first_name":"Christophe","last_name":"Chipot"},{"last_name":"Singharoy","first_name":"Abhishek","full_name":"Singharoy, Abhishek"}],"related_material":{"record":[{"relation":"used_in_publication","id":"8040","status":"public"}]},"article_processing_charge":"No","year":"2020","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","title":"Charge transfer and chemo-mechanical coupling in respiratory complex I","status":"public","day":"20","tmp":{"image":"/images/cc_by_nc.png","legal_code_url":"https://creativecommons.org/licenses/by-nc/4.0/legalcode","name":"Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)","short":"CC BY-NC (4.0)"},"publisher":"American Chemical Society","oa_version":"Published Version","date_created":"2021-04-14T12:05:20Z","type":"research_data_reference","doi":"10.1021/jacs.9b13450.s002","abstract":[{"lang":"eng","text":"The mitochondrial respiratory chain, formed by five protein complexes, utilizes energy from catabolic processes to synthesize ATP. Complex I, the first and the largest protein complex of the chain, harvests electrons from NADH to reduce quinone, while pumping protons across the mitochondrial membrane. Detailed knowledge of the working principle of such coupled charge-transfer processes remains, however, fragmentary due to bottlenecks in understanding redox-driven conformational transitions and their interplay with the hydrated proton pathways. Complex I from Thermus thermophilus encases 16 subunits with nine iron–sulfur clusters, reduced by electrons from NADH. Here, employing the latest crystal structure of T. thermophilus complex I, we have used microsecond-scale molecular dynamics simulations to study the chemo-mechanical coupling between redox changes of the iron–sulfur clusters and conformational transitions across complex I. First, we identify the redox switches within complex I, which allosterically couple the dynamics of the quinone binding pocket to the site of NADH reduction. Second, our free-energy calculations reveal that the affinity of the quinone, specifically menaquinone, for the binding-site is higher than that of its reduced, menaquinol forma design essential for menaquinol release. Remarkably, the barriers to diffusive menaquinone dynamics are lesser than that of the more ubiquitous ubiquinone, and the naphthoquinone headgroup of the former furnishes stronger binding interactions with the pocket, favoring menaquinone for charge transport in T. thermophilus. Our computations are consistent with experimentally validated mutations and hierarchize the key residues into three functional classes, identifying new mutation targets. Third, long-range hydrogen-bond networks connecting the quinone-binding site to the transmembrane subunits are found to be responsible for proton pumping. Put together, the simulations reveal the molecular design principles linking redox reactions to quinone turnover to proton translocation in complex I."}],"_id":"9326","date_updated":"2025-07-10T11:55:01Z","date_published":"2020-05-20T00:00:00Z","citation":{"apa":"Gupta, C., Khaniya, U., Chan, C., Dehez, F., Shekhar, M., Gunner, M. R., … Singharoy, A. (2020). Charge transfer and chemo-mechanical coupling in respiratory complex I. American Chemical Society. https://doi.org/10.1021/jacs.9b13450.s002","short":"C. Gupta, U. Khaniya, C. Chan, F. Dehez, M. Shekhar, M.R. Gunner, L.A. Sazanov, C. Chipot, A. Singharoy, (2020).","mla":"Gupta, Chitrak, et al. Charge Transfer and Chemo-Mechanical Coupling in Respiratory Complex I. American Chemical Society, 2020, doi:10.1021/jacs.9b13450.s002.","ama":"Gupta C, Khaniya U, Chan C, et al. Charge transfer and chemo-mechanical coupling in respiratory complex I. 2020. doi:10.1021/jacs.9b13450.s002","ieee":"C. Gupta et al., “Charge transfer and chemo-mechanical coupling in respiratory complex I.” American Chemical Society, 2020.","chicago":"Gupta, Chitrak, Umesh Khaniya, Chun Chan, Francois Dehez, Mrinal Shekhar, M. R. Gunner, Leonid A Sazanov, Christophe Chipot, and Abhishek Singharoy. “Charge Transfer and Chemo-Mechanical Coupling in Respiratory Complex I.” American Chemical Society, 2020. https://doi.org/10.1021/jacs.9b13450.s002.","ista":"Gupta C, Khaniya U, Chan C, Dehez F, Shekhar M, Gunner MR, Sazanov LA, Chipot C, Singharoy A. 2020. Charge transfer and chemo-mechanical coupling in respiratory complex I, American Chemical Society, 10.1021/jacs.9b13450.s002."},"department":[{"_id":"LeSa"}],"license":"https://creativecommons.org/licenses/by-nc/4.0/","month":"05"},{"file":[{"relation":"main_file","date_created":"2021-05-25T09:51:36Z","file_id":"9421","checksum":"2aaaa7d7226e49161311d91627cf783b","content_type":"application/pdf","creator":"kschuh","date_updated":"2021-05-25T09:51:36Z","success":1,"file_name":"2020_PMLR_Kurtz.pdf","access_level":"open_access","file_size":741899}],"publication_identifier":{"issn":["2640-3498"]},"day":"12","status":"public","publication":"37th International Conference on Machine Learning, ICML 2020","volume":119,"ddc":["000"],"quality_controlled":"1","file_date_updated":"2021-05-25T09:51:36Z","month":"07","scopus_import":"1","conference":{"name":"ICML: International Conference on Machine Learning","end_date":"2020-07-18","location":"Online","start_date":"2020-07-12"},"user_id":"3E5EF7F0-F248-11E8-B48F-1D18A9856A87","title":"Inducing and exploiting activation sparsity for fast neural network inference","intvolume":" 119","author":[{"last_name":"Kurtz","first_name":"Mark","full_name":"Kurtz, Mark"},{"full_name":"Kopinsky, Justin","first_name":"Justin","last_name":"Kopinsky"},{"last_name":"Gelashvili","first_name":"Rati","full_name":"Gelashvili, Rati"},{"full_name":"Matveev, Alexander","first_name":"Alexander","last_name":"Matveev"},{"full_name":"Carr, John","last_name":"Carr","first_name":"John"},{"last_name":"Goin","first_name":"Michael","full_name":"Goin, Michael"},{"last_name":"Leiserson","first_name":"William","full_name":"Leiserson, William"},{"first_name":"Sage","last_name":"Moore","full_name":"Moore, Sage"},{"first_name":"Bill","last_name":"Nell","full_name":"Nell, Bill"},{"full_name":"Shavit, Nir","first_name":"Nir","last_name":"Shavit"},{"orcid":"0000-0003-3650-940X","last_name":"Alistarh","first_name":"Dan-Adrian","full_name":"Alistarh, Dan-Adrian","id":"4A899BFC-F248-11E8-B48F-1D18A9856A87"}],"year":"2020","article_processing_charge":"No","has_accepted_license":"1","language":[{"iso":"eng"}],"page":"5533-5543","department":[{"_id":"DaAl"}],"date_published":"2020-07-12T00:00:00Z","citation":{"short":"M. Kurtz, J. Kopinsky, R. Gelashvili, A. Matveev, J. Carr, M. Goin, W. Leiserson, S. Moore, B. Nell, N. Shavit, D.-A. Alistarh, in:, 37th International Conference on Machine Learning, ICML 2020, 2020, pp. 5533–5543.","mla":"Kurtz, Mark, et al. “Inducing and Exploiting Activation Sparsity for Fast Neural Network Inference.” 37th International Conference on Machine Learning, ICML 2020, vol. 119, 2020, pp. 5533–43.","apa":"Kurtz, M., Kopinsky, J., Gelashvili, R., Matveev, A., Carr, J., Goin, M., … Alistarh, D.-A. (2020). Inducing and exploiting activation sparsity for fast neural network inference. In 37th International Conference on Machine Learning, ICML 2020 (Vol. 119, pp. 5533–5543). Online.","ista":"Kurtz M, Kopinsky J, Gelashvili R, Matveev A, Carr J, Goin M, Leiserson W, Moore S, Nell B, Shavit N, Alistarh D-A. 2020. Inducing and exploiting activation sparsity for fast neural network inference. 37th International Conference on Machine Learning, ICML 2020. ICML: International Conference on Machine Learning vol. 119, 5533–5543.","chicago":"Kurtz, Mark, Justin Kopinsky, Rati Gelashvili, Alexander Matveev, John Carr, Michael Goin, William Leiserson, et al. “Inducing and Exploiting Activation Sparsity for Fast Neural Network Inference.” In 37th International Conference on Machine Learning, ICML 2020, 119:5533–43, 2020.","ama":"Kurtz M, Kopinsky J, Gelashvili R, et al. Inducing and exploiting activation sparsity for fast neural network inference. In: 37th International Conference on Machine Learning, ICML 2020. Vol 119. ; 2020:5533-5543.","ieee":"M. Kurtz et al., “Inducing and exploiting activation sparsity for fast neural network inference,” in 37th International Conference on Machine Learning, ICML 2020, Online, 2020, vol. 119, pp. 5533–5543."},"date_updated":"2023-02-23T13:57:24Z","_id":"9415","abstract":[{"text":"Optimizing convolutional neural networks for fast inference has recently become an extremely active area of research. One of the go-to solutions in this context is weight pruning, which aims to reduce computational and memory footprint by removing large subsets of the connections in a neural network. Surprisingly, much less attention has been given to exploiting sparsity in the activation maps, which tend to be naturally sparse in many settings thanks to the structure of rectified linear (ReLU) activation functions. In this paper, we present an in-depth analysis of methods for maximizing the sparsity of the activations in a trained neural network, and show that, when coupled with an efficient sparse-input convolution algorithm, we can leverage this sparsity for significant performance gains. To induce highly sparse activation maps without accuracy loss, we introduce a new regularization technique, coupled with a new threshold-based sparsification method based on a parameterized activation function called Forced-Activation-Threshold Rectified Linear Unit (FATReLU). We examine the impact of our methods on popular image classification models, showing that most architectures can adapt to significantly sparser activation maps without any accuracy loss. Our second contribution is showing that these these compression gains can be translated into inference speedups: we provide a new algorithm to enable fast convolution operations over networks with sparse activations, and show that it can enable significant speedups for end-to-end inference on a range of popular models on the large-scale ImageNet image classification task on modern Intel CPUs, with little or no retraining cost. ","lang":"eng"}],"oa":1,"type":"conference","oa_version":"Published Version","date_created":"2021-05-23T22:01:45Z"},{"year":"2020","article_processing_charge":"No","extern":"1","author":[{"full_name":"Choi, Jaemyung","last_name":"Choi","first_name":"Jaemyung"},{"first_name":"David B.","last_name":"Lyons","full_name":"Lyons, David B."},{"last_name":"Kim","first_name":"M. Yvonne","full_name":"Kim, M. Yvonne"},{"full_name":"Moore, Jonathan D.","last_name":"Moore","first_name":"Jonathan D."},{"orcid":"0000-0002-0123-8649","last_name":"Zilberman","first_name":"Daniel","full_name":"Zilberman, Daniel","id":"6973db13-dd5f-11ea-814e-b3e5455e9ed1"}],"intvolume":" 77","article_type":"original","user_id":"0043cee0-e5fc-11ee-9736-f83bc23afbf0","title":"DNA methylation and histone H1 jointly repress transposable elements and aberrant intragenic transcripts","scopus_import":"1","date_created":"2021-06-08T06:37:09Z","oa_version":"Published Version","type":"journal_article","issue":"2","oa":1,"date_published":"2020-01-16T00:00:00Z","department":[{"_id":"DaZi"}],"page":"310-323.e7","citation":{"short":"J. Choi, D.B. Lyons, M.Y. Kim, J.D. Moore, D. Zilberman, Molecular Cell 77 (2020) 310–323.e7.","mla":"Choi, Jaemyung, et al. “DNA Methylation and Histone H1 Jointly Repress Transposable Elements and Aberrant Intragenic Transcripts.” Molecular Cell, vol. 77, no. 2, Elsevier, 2020, p. 310–323.e7, doi:10.1016/j.molcel.2019.10.011.","apa":"Choi, J., Lyons, D. B., Kim, M. Y., Moore, J. D., & Zilberman, D. (2020). DNA methylation and histone H1 jointly repress transposable elements and aberrant intragenic transcripts. Molecular Cell. Elsevier. https://doi.org/10.1016/j.molcel.2019.10.011","ista":"Choi J, Lyons DB, Kim MY, Moore JD, Zilberman D. 2020. DNA methylation and histone H1 jointly repress transposable elements and aberrant intragenic transcripts. Molecular Cell. 77(2), 310–323.e7.","chicago":"Choi, Jaemyung, David B. Lyons, M. Yvonne Kim, Jonathan D. Moore, and Daniel Zilberman. “DNA Methylation and Histone H1 Jointly Repress Transposable Elements and Aberrant Intragenic Transcripts.” Molecular Cell. Elsevier, 2020. https://doi.org/10.1016/j.molcel.2019.10.011.","ieee":"J. Choi, D. B. Lyons, M. Y. Kim, J. D. Moore, and D. Zilberman, “DNA methylation and histone H1 jointly repress transposable elements and aberrant intragenic transcripts,” Molecular Cell, vol. 77, no. 2. Elsevier, p. 310–323.e7, 2020.","ama":"Choi J, Lyons DB, Kim MY, Moore JD, Zilberman D. DNA methylation and histone H1 jointly repress transposable elements and aberrant intragenic transcripts. Molecular Cell. 2020;77(2):310-323.e7. doi:10.1016/j.molcel.2019.10.011"},"date_updated":"2024-10-16T12:14:37Z","_id":"9526","abstract":[{"text":"DNA methylation and histone H1 mediate transcriptional silencing of genes and transposable elements, but how they interact is unclear. In plants and animals with mosaic genomic methylation, functionally mysterious methylation is also common within constitutively active housekeeping genes. Here, we show that H1 is enriched in methylated sequences, including genes, of Arabidopsis thaliana, yet this enrichment is independent of DNA methylation. Loss of H1 disperses heterochromatin, globally alters nucleosome organization, and activates H1-bound genes, but only weakly de-represses transposable elements. However, H1 loss strongly activates transposable elements hypomethylated through mutation of DNA methyltransferase MET1. Hypomethylation of genes also activates antisense transcription, which is modestly enhanced by H1 loss. Our results demonstrate that H1 and DNA methylation jointly maintain transcriptional homeostasis by silencing transposable elements and aberrant intragenic transcripts. Such functionality plausibly explains why DNA methylation, a well-known mutagen, has been maintained within coding sequences of crucial plant and animal genes.","lang":"eng"}],"language":[{"iso":"eng"}],"quality_controlled":"1","external_id":{"pmid":["31732458"]},"publication":"Molecular Cell","volume":77,"publisher":"Elsevier","publication_status":"published","pmid":1,"day":"16","status":"public","publication_identifier":{"eissn":["1097-4164"],"issn":["1097-2765"]},"OA_place":"publisher","main_file_link":[{"url":"https://doi.org/10.1016/j.molcel.2019.10.011","open_access":"1"}],"month":"01","doi":"10.1016/j.molcel.2019.10.011","OA_type":"hybrid"},{"scopus_import":"1","title":"Universality of random permutations","user_id":"6785fbc1-c503-11eb-8a32-93094b40e1cf","extern":"1","year":"2020","article_processing_charge":"No","article_type":"original","intvolume":" 52","author":[{"full_name":"He, Xiaoyu","last_name":"He","first_name":"Xiaoyu"},{"full_name":"Kwan, Matthew Alan","id":"5fca0887-a1db-11eb-95d1-ca9d5e0453b3","last_name":"Kwan","orcid":"0000-0002-4003-7567","first_name":"Matthew Alan"}],"language":[{"iso":"eng"}],"citation":{"apa":"He, X., & Kwan, M. A. (2020). Universality of random permutations. Bulletin of the London Mathematical Society. Wiley. https://doi.org/10.1112/blms.12345","short":"X. He, M.A. Kwan, Bulletin of the London Mathematical Society 52 (2020) 515–529.","mla":"He, Xiaoyu, and Matthew Alan Kwan. “Universality of Random Permutations.” Bulletin of the London Mathematical Society, vol. 52, no. 3, Wiley, 2020, pp. 515–29, doi:10.1112/blms.12345.","ieee":"X. He and M. A. Kwan, “Universality of random permutations,” Bulletin of the London Mathematical Society, vol. 52, no. 3. Wiley, pp. 515–529, 2020.","ama":"He X, Kwan MA. Universality of random permutations. Bulletin of the London Mathematical Society. 2020;52(3):515-529. doi:10.1112/blms.12345","chicago":"He, Xiaoyu, and Matthew Alan Kwan. “Universality of Random Permutations.” Bulletin of the London Mathematical Society. Wiley, 2020. https://doi.org/10.1112/blms.12345.","ista":"He X, Kwan MA. 2020. Universality of random permutations. Bulletin of the London Mathematical Society. 52(3), 515–529."},"date_published":"2020-06-01T00:00:00Z","page":"515-529","date_updated":"2023-02-23T14:01:23Z","_id":"9573","abstract":[{"lang":"eng","text":"It is a classical fact that for any ε>0, a random permutation of length n=(1+ε)k2/4 typically contains a monotone subsequence of length k. As a far-reaching generalization, Alon conjectured that a random permutation of this same length n is typically k-universal, meaning that it simultaneously contains every pattern of length k. He also made the simple observation that for n=O(k2logk), a random length-n permutation is typically k-universal. We make the first significant progress towards Alon's conjecture by showing that n=2000k2loglogk suffices."}],"type":"journal_article","date_created":"2021-06-21T06:23:42Z","oa_version":"Preprint","oa":1,"issue":"3","publisher":"Wiley","publication_status":"published","publication":"Bulletin of the London Mathematical Society","volume":52,"arxiv":1,"publication_identifier":{"issn":["0024-6093"],"eissn":["1469-2120"]},"day":"01","status":"public","external_id":{"arxiv":["1911.12878"]},"quality_controlled":"1","month":"06","doi":"10.1112/blms.12345","main_file_link":[{"open_access":"1","url":"https://arxiv.org/abs/1911.12878"}]},{"main_file_link":[{"open_access":"1","url":"http://arxiv-export-lb.library.cornell.edu/abs/1810.07462"}],"doi":"10.1093/imrn/rnaa004","month":"11","quality_controlled":"1","external_id":{"arxiv":["1810.07462"]},"day":"01","status":"public","arxiv":1,"publication_identifier":{"eissn":["1687-0247"],"issn":["1073-7928"]},"publication":"International Mathematics Research Notices","volume":2020,"publisher":"Oxford University Press","publication_status":"published","issue":"21","oa":1,"oa_version":"Preprint","date_created":"2021-06-21T08:12:30Z","type":"journal_article","citation":{"apa":"Bucić, M., Kwan, M. A., Pokrovskiy, A., & Sudakov, B. (2020). Halfway to Rota’s basis conjecture. International Mathematics Research Notices. Oxford University Press. https://doi.org/10.1093/imrn/rnaa004","short":"M. Bucić, M.A. Kwan, A. Pokrovskiy, B. Sudakov, International Mathematics Research Notices 2020 (2020) 8007–8026.","mla":"Bucić, Matija, et al. “Halfway to Rota’s Basis Conjecture.” International Mathematics Research Notices, vol. 2020, no. 21, Oxford University Press, 2020, pp. 8007–26, doi:10.1093/imrn/rnaa004.","chicago":"Bucić, Matija, Matthew Alan Kwan, Alexey Pokrovskiy, and Benny Sudakov. “Halfway to Rota’s Basis Conjecture.” International Mathematics Research Notices. Oxford University Press, 2020. https://doi.org/10.1093/imrn/rnaa004.","ista":"Bucić M, Kwan MA, Pokrovskiy A, Sudakov B. 2020. Halfway to Rota’s basis conjecture. International Mathematics Research Notices. 2020(21), 8007–8026.","ama":"Bucić M, Kwan MA, Pokrovskiy A, Sudakov B. Halfway to Rota’s basis conjecture. International Mathematics Research Notices. 2020;2020(21):8007-8026. doi:10.1093/imrn/rnaa004","ieee":"M. Bucić, M. A. Kwan, A. Pokrovskiy, and B. Sudakov, “Halfway to Rota’s basis conjecture,” International Mathematics Research Notices, vol. 2020, no. 21. Oxford University Press, pp. 8007–8026, 2020."},"page":"8007-8026","date_published":"2020-11-01T00:00:00Z","_id":"9576","abstract":[{"text":"In 1989, Rota made the following conjecture. Given n bases B1,…,Bn in an n-dimensional vector space V⁠, one can always find n disjoint bases of V⁠, each containing exactly one element from each Bi (we call such bases transversal bases). Rota’s basis conjecture remains wide open despite its apparent simplicity and the efforts of many researchers (e.g., the conjecture was recently the subject of the collaborative “Polymath” project). In this paper we prove that one can always find (1/2−o(1))n disjoint transversal bases, improving on the previous best bound of Ω(n/logn)⁠. Our results also apply to the more general setting of matroids.","lang":"eng"}],"date_updated":"2023-02-23T14:01:30Z","language":[{"iso":"eng"}],"intvolume":" 2020","author":[{"full_name":"Bucić, Matija","first_name":"Matija","last_name":"Bucić"},{"first_name":"Matthew Alan","orcid":"0000-0002-4003-7567","last_name":"Kwan","id":"5fca0887-a1db-11eb-95d1-ca9d5e0453b3","full_name":"Kwan, Matthew Alan"},{"full_name":"Pokrovskiy, Alexey","first_name":"Alexey","last_name":"Pokrovskiy"},{"full_name":"Sudakov, Benny","last_name":"Sudakov","first_name":"Benny"}],"article_type":"original","year":"2020","article_processing_charge":"No","extern":"1","user_id":"6785fbc1-c503-11eb-8a32-93094b40e1cf","title":"Halfway to Rota’s basis conjecture","scopus_import":"1"},{"external_id":{"arxiv":["1711.02937"]},"quality_controlled":"1","publisher":"Oxford University Press","publication_status":"published","publication":"International Mathematics Research Notices","volume":2020,"arxiv":1,"publication_identifier":{"issn":["1073-7928"],"eissn":["1687-0247"]},"day":"01","status":"public","OA_place":"publisher","main_file_link":[{"open_access":"1","url":"https://doi.org/10.1093/imrn/rny064"}],"month":"03","OA_type":"hybrid","doi":"10.1093/imrn/rny064","extern":"1","year":"2020","article_processing_charge":"No","article_type":"original","intvolume":" 2020","author":[{"first_name":"Matthew Alan","orcid":"0000-0002-4003-7567","last_name":"Kwan","full_name":"Kwan, Matthew Alan","id":"5fca0887-a1db-11eb-95d1-ca9d5e0453b3"},{"first_name":"Benny","last_name":"Sudakov","full_name":"Sudakov, Benny"}],"scopus_import":"1","user_id":"0043cee0-e5fc-11ee-9736-f83bc23afbf0","title":"Ramsey graphs induce subgraphs of quadratically many sizes","type":"journal_article","oa_version":"Published Version","date_created":"2021-06-21T08:30:12Z","oa":1,"issue":"6","language":[{"iso":"eng"}],"page":"1621–1638","citation":{"ieee":"M. A. Kwan and B. Sudakov, “Ramsey graphs induce subgraphs of quadratically many sizes,” International Mathematics Research Notices, vol. 2020, no. 6. Oxford University Press, pp. 1621–1638, 2020.","ama":"Kwan MA, Sudakov B. Ramsey graphs induce subgraphs of quadratically many sizes. International Mathematics Research Notices. 2020;2020(6):1621–1638. doi:10.1093/imrn/rny064","ista":"Kwan MA, Sudakov B. 2020. Ramsey graphs induce subgraphs of quadratically many sizes. International Mathematics Research Notices. 2020(6), 1621–1638.","chicago":"Kwan, Matthew Alan, and Benny Sudakov. “Ramsey Graphs Induce Subgraphs of Quadratically Many Sizes.” International Mathematics Research Notices. Oxford University Press, 2020. https://doi.org/10.1093/imrn/rny064.","mla":"Kwan, Matthew Alan, and Benny Sudakov. “Ramsey Graphs Induce Subgraphs of Quadratically Many Sizes.” International Mathematics Research Notices, vol. 2020, no. 6, Oxford University Press, 2020, pp. 1621–1638, doi:10.1093/imrn/rny064.","short":"M.A. Kwan, B. Sudakov, International Mathematics Research Notices 2020 (2020) 1621–1638.","apa":"Kwan, M. A., & Sudakov, B. (2020). Ramsey graphs induce subgraphs of quadratically many sizes. International Mathematics Research Notices. Oxford University Press. https://doi.org/10.1093/imrn/rny064"},"date_published":"2020-03-01T00:00:00Z","date_updated":"2024-10-16T12:20:07Z","_id":"9577","abstract":[{"text":"An n-vertex graph is called C-Ramsey if it has no clique or independent set of size Clogn⁠. All known constructions of Ramsey graphs involve randomness in an essential way, and there is an ongoing line of research towards showing that in fact all Ramsey graphs must obey certain “richness” properties characteristic of random graphs. Motivated by an old problem of Erd̋s and McKay, recently Narayanan, Sahasrabudhe, and Tomon conjectured that for any fixed C, every n-vertex C-Ramsey graph induces subgraphs of Θ(n2) different sizes. In this paper we prove this conjecture.","lang":"eng"}]},{"citation":{"ieee":"M. Bucić, M. A. Kwan, A. Pokrovskiy, B. Sudakov, T. Tran, and A. Z. Wagner, “Nearly-linear monotone paths in edge-ordered graphs,” Israel Journal of Mathematics, vol. 238, no. 2. Springer, pp. 663–685, 2020.","ama":"Bucić M, Kwan MA, Pokrovskiy A, Sudakov B, Tran T, Wagner AZ. Nearly-linear monotone paths in edge-ordered graphs. Israel Journal of Mathematics. 2020;238(2):663-685. doi:10.1007/s11856-020-2035-7","ista":"Bucić M, Kwan MA, Pokrovskiy A, Sudakov B, Tran T, Wagner AZ. 2020. Nearly-linear monotone paths in edge-ordered graphs. Israel Journal of Mathematics. 238(2), 663–685.","chicago":"Bucić, Matija, Matthew Alan Kwan, Alexey Pokrovskiy, Benny Sudakov, Tuan Tran, and Adam Zsolt Wagner. “Nearly-Linear Monotone Paths in Edge-Ordered Graphs.” Israel Journal of Mathematics. Springer, 2020. https://doi.org/10.1007/s11856-020-2035-7.","short":"M. Bucić, M.A. Kwan, A. Pokrovskiy, B. Sudakov, T. Tran, A.Z. Wagner, Israel Journal of Mathematics 238 (2020) 663–685.","mla":"Bucić, Matija, et al. “Nearly-Linear Monotone Paths in Edge-Ordered Graphs.” Israel Journal of Mathematics, vol. 238, no. 2, Springer, 2020, pp. 663–85, doi:10.1007/s11856-020-2035-7.","apa":"Bucić, M., Kwan, M. A., Pokrovskiy, A., Sudakov, B., Tran, T., & Wagner, A. Z. (2020). Nearly-linear monotone paths in edge-ordered graphs. Israel Journal of Mathematics. Springer. https://doi.org/10.1007/s11856-020-2035-7"},"page":"663-685","date_published":"2020-07-01T00:00:00Z","abstract":[{"lang":"eng","text":"How long a monotone path can one always find in any edge-ordering of the complete graph Kn? This appealing question was first asked by Chvátal and Komlós in 1971, and has since attracted the attention of many researchers, inspiring a variety of related problems. The prevailing conjecture is that one can always find a monotone path of linear length, but until now the best known lower bound was n2/3-o(1). In this paper we almost close this gap, proving that any edge-ordering of the complete graph contains a monotone path of length n1-o(1)."}],"_id":"9578","date_updated":"2023-02-23T14:01:35Z","language":[{"iso":"eng"}],"date_created":"2021-06-21T13:24:35Z","oa_version":"Preprint","type":"journal_article","issue":"2","oa":1,"user_id":"6785fbc1-c503-11eb-8a32-93094b40e1cf","title":"Nearly-linear monotone paths in edge-ordered graphs","scopus_import":"1","year":"2020","article_processing_charge":"No","extern":"1","intvolume":" 238","author":[{"first_name":"Matija","last_name":"Bucić","full_name":"Bucić, Matija"},{"full_name":"Kwan, Matthew Alan","id":"5fca0887-a1db-11eb-95d1-ca9d5e0453b3","last_name":"Kwan","orcid":"0000-0002-4003-7567","first_name":"Matthew Alan"},{"first_name":"Alexey","last_name":"Pokrovskiy","full_name":"Pokrovskiy, Alexey"},{"full_name":"Sudakov, Benny","first_name":"Benny","last_name":"Sudakov"},{"first_name":"Tuan","last_name":"Tran","full_name":"Tran, Tuan"},{"full_name":"Wagner, Adam Zsolt","first_name":"Adam Zsolt","last_name":"Wagner"}],"article_type":"original","month":"07","doi":"10.1007/s11856-020-2035-7","main_file_link":[{"url":"https://arxiv.org/abs/1809.01468","open_access":"1"}],"publication":"Israel Journal of Mathematics","volume":238,"publisher":"Springer","publication_status":"published","day":"01","status":"public","arxiv":1,"publication_identifier":{"eissn":["1565-8511"],"issn":["0021-2172"]},"quality_controlled":"1","external_id":{"arxiv":["1809.01468"]}},{"article_type":"original","author":[{"id":"5fca0887-a1db-11eb-95d1-ca9d5e0453b3","full_name":"Kwan, Matthew Alan","last_name":"Kwan","orcid":"0000-0002-4003-7567","first_name":"Matthew Alan"}],"intvolume":" 121","extern":"1","year":"2020","article_processing_charge":"No","scopus_import":"1","title":"Almost all Steiner triple systems have perfect matchings","user_id":"6785fbc1-c503-11eb-8a32-93094b40e1cf","oa":1,"issue":"6","type":"journal_article","oa_version":"Preprint","date_created":"2021-06-22T06:35:16Z","language":[{"iso":"eng"}],"page":"1468-1495","citation":{"ama":"Kwan MA. Almost all Steiner triple systems have perfect matchings. Proceedings of the London Mathematical Society. 2020;121(6):1468-1495. doi:10.1112/plms.12373","ieee":"M. A. Kwan, “Almost all Steiner triple systems have perfect matchings,” Proceedings of the London Mathematical Society, vol. 121, no. 6. Wiley, pp. 1468–1495, 2020.","chicago":"Kwan, Matthew Alan. “Almost All Steiner Triple Systems Have Perfect Matchings.” Proceedings of the London Mathematical Society. Wiley, 2020. https://doi.org/10.1112/plms.12373.","ista":"Kwan MA. 2020. Almost all Steiner triple systems have perfect matchings. Proceedings of the London Mathematical Society. 121(6), 1468–1495.","apa":"Kwan, M. A. (2020). Almost all Steiner triple systems have perfect matchings. Proceedings of the London Mathematical Society. Wiley. https://doi.org/10.1112/plms.12373","mla":"Kwan, Matthew Alan. “Almost All Steiner Triple Systems Have Perfect Matchings.” Proceedings of the London Mathematical Society, vol. 121, no. 6, Wiley, 2020, pp. 1468–95, doi:10.1112/plms.12373.","short":"M.A. Kwan, Proceedings of the London Mathematical Society 121 (2020) 1468–1495."},"date_published":"2020-12-01T00:00:00Z","_id":"9581","date_updated":"2023-02-23T14:01:43Z","abstract":[{"lang":"eng","text":"We show that for any 𝑛 divisible by 3, almost all order- 𝑛 Steiner triple systems have a perfect matching (also known as a parallel class or resolution class). In fact, we prove a general upper bound on the number of perfect matchings in a Steiner triple system and show that almost all Steiner triple systems essentially attain this maximum. We accomplish this via a general theorem comparing a uniformly random Steiner triple system to the outcome of the triangle removal process, which we hope will be useful for other problems. Our methods can also be adapted to other types of designs; for example, we sketch a proof of the theorem that almost all Latin squares have transversals."}],"external_id":{"arxiv":["1611.02246"]},"quality_controlled":"1","arxiv":1,"publication_identifier":{"eissn":["1460-244X"],"issn":["0024-6115"]},"day":"01","status":"public","publisher":"Wiley","publication_status":"published","publication":"Proceedings of the London Mathematical Society","volume":121,"main_file_link":[{"url":"https://arxiv.org/abs/1611.02246","open_access":"1"}],"doi":"10.1112/plms.12373","month":"12"},{"extern":"1","article_processing_charge":"No","year":"2020","article_type":"original","author":[{"first_name":"Matthew Alan","last_name":"Kwan","orcid":"0000-0002-4003-7567","full_name":"Kwan, Matthew Alan","id":"5fca0887-a1db-11eb-95d1-ca9d5e0453b3"},{"full_name":"Letzter, Shoham","last_name":"Letzter","first_name":"Shoham"},{"last_name":"Sudakov","first_name":"Benny","full_name":"Sudakov, Benny"},{"full_name":"Tran, Tuan","last_name":"Tran","first_name":"Tuan"}],"intvolume":" 40","scopus_import":"1","user_id":"6785fbc1-c503-11eb-8a32-93094b40e1cf","title":"Dense induced bipartite subgraphs in triangle-free graphs","type":"journal_article","oa_version":"Preprint","date_created":"2021-06-22T06:42:26Z","oa":1,"issue":"2","language":[{"iso":"eng"}],"_id":"9582","date_updated":"2023-02-23T14:01:45Z","abstract":[{"text":"The problem of finding dense induced bipartite subgraphs in H-free graphs has a long history, and was posed 30 years ago by Erdős, Faudree, Pach and Spencer. In this paper, we obtain several results in this direction. First we prove that any H-free graph with minimum degree at least d contains an induced bipartite subgraph of minimum degree at least cH log d/log log d, thus nearly confirming one and proving another conjecture of Esperet, Kang and Thomassé. Complementing this result, we further obtain optimal bounds for this problem in the case of dense triangle-free graphs, and we also answer a question of Erdœs, Janson, Łuczak and Spencer.","lang":"eng"}],"page":"283-305","date_published":"2020-04-01T00:00:00Z","citation":{"mla":"Kwan, Matthew Alan, et al. “Dense Induced Bipartite Subgraphs in Triangle-Free Graphs.” Combinatorica, vol. 40, no. 2, Springer, 2020, pp. 283–305, doi:10.1007/s00493-019-4086-0.","short":"M.A. Kwan, S. Letzter, B. Sudakov, T. Tran, Combinatorica 40 (2020) 283–305.","apa":"Kwan, M. A., Letzter, S., Sudakov, B., & Tran, T. (2020). Dense induced bipartite subgraphs in triangle-free graphs. Combinatorica. Springer. https://doi.org/10.1007/s00493-019-4086-0","ista":"Kwan MA, Letzter S, Sudakov B, Tran T. 2020. Dense induced bipartite subgraphs in triangle-free graphs. Combinatorica. 40(2), 283–305.","chicago":"Kwan, Matthew Alan, Shoham Letzter, Benny Sudakov, and Tuan Tran. “Dense Induced Bipartite Subgraphs in Triangle-Free Graphs.” Combinatorica. Springer, 2020. https://doi.org/10.1007/s00493-019-4086-0.","ama":"Kwan MA, Letzter S, Sudakov B, Tran T. Dense induced bipartite subgraphs in triangle-free graphs. Combinatorica. 2020;40(2):283-305. doi:10.1007/s00493-019-4086-0","ieee":"M. A. Kwan, S. Letzter, B. Sudakov, and T. Tran, “Dense induced bipartite subgraphs in triangle-free graphs,” Combinatorica, vol. 40, no. 2. Springer, pp. 283–305, 2020."},"external_id":{"arxiv":["1810.12144"]},"quality_controlled":"1","publication_status":"published","publisher":"Springer","volume":40,"publication":"Combinatorica","publication_identifier":{"eissn":["1439-6912"],"issn":["0209-9683"]},"arxiv":1,"status":"public","day":"01","main_file_link":[{"url":"https://arxiv.org/abs/1810.12144","open_access":"1"}],"month":"04","doi":"10.1007/s00493-019-4086-0"},{"quality_controlled":"1","ddc":["510"],"external_id":{"pmid":["1907.06744"]},"pmid":1,"day":"03","status":"public","file":[{"date_created":"2021-06-22T09:23:59Z","relation":"main_file","file_id":"9584","content_type":"application/pdf","checksum":"5553c596bb4db0f38226a56bee9c87a1","creator":"asandaue","date_updated":"2021-06-22T09:23:59Z","access_level":"open_access","file_name":"2020_CambridgeUniversityPress_Ferber.pdf","success":1,"file_size":601516}],"publication_identifier":{"eissn":["2050-5094"]},"publication":"Forum of Mathematics","tmp":{"legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","image":"/images/cc_by.png","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","short":"CC BY (4.0)"},"volume":8,"publisher":"Cambridge University Press","publication_status":"published","OA_place":"publisher","doi":"10.1017/fms.2020.29","file_date_updated":"2021-06-22T09:23:59Z","OA_type":"gold","month":"11","license":"https://creativecommons.org/licenses/by/4.0/","intvolume":" 8","author":[{"first_name":"Asaf","last_name":"Ferber","full_name":"Ferber, Asaf"},{"id":"5fca0887-a1db-11eb-95d1-ca9d5e0453b3","full_name":"Kwan, Matthew Alan","last_name":"Kwan","orcid":"0000-0002-4003-7567","first_name":"Matthew Alan"}],"article_type":"original","year":"2020","article_processing_charge":"No","extern":"1","title":"Almost all Steiner triple systems are almost resolvable","user_id":"0043cee0-e5fc-11ee-9736-f83bc23afbf0","scopus_import":"1","oa":1,"oa_version":"Published Version","date_created":"2021-06-22T09:12:23Z","type":"journal_article","article_number":"e39","date_published":"2020-11-03T00:00:00Z","citation":{"apa":"Ferber, A., & Kwan, M. A. (2020). Almost all Steiner triple systems are almost resolvable. Forum of Mathematics. Cambridge University Press. https://doi.org/10.1017/fms.2020.29","short":"A. Ferber, M.A. Kwan, Forum of Mathematics 8 (2020).","mla":"Ferber, Asaf, and Matthew Alan Kwan. “Almost All Steiner Triple Systems Are Almost Resolvable.” Forum of Mathematics, vol. 8, e39, Cambridge University Press, 2020, doi:10.1017/fms.2020.29.","chicago":"Ferber, Asaf, and Matthew Alan Kwan. “Almost All Steiner Triple Systems Are Almost Resolvable.” Forum of Mathematics. Cambridge University Press, 2020. https://doi.org/10.1017/fms.2020.29.","ista":"Ferber A, Kwan MA. 2020. Almost all Steiner triple systems are almost resolvable. Forum of Mathematics. 8, e39.","ama":"Ferber A, Kwan MA. Almost all Steiner triple systems are almost resolvable. Forum of Mathematics. 2020;8. doi:10.1017/fms.2020.29","ieee":"A. Ferber and M. A. Kwan, “Almost all Steiner triple systems are almost resolvable,” Forum of Mathematics, vol. 8. Cambridge University Press, 2020."},"abstract":[{"text":"We show that for any n divisible by 3, almost all order-n Steiner triple systems admit a decomposition of almost all their triples into disjoint perfect matchings (that is, almost all Steiner triple systems are almost resolvable).","lang":"eng"}],"_id":"9583","date_updated":"2024-10-16T12:26:40Z","DOAJ_listed":"1","has_accepted_license":"1","language":[{"iso":"eng"}]},{"volume":33,"publication_status":"published","publisher":"Neural Information Processing Systems Foundation","status":"public","day":"06","publication_identifier":{"isbn":["9781713829546"],"issn":["1049-5258"]},"arxiv":1,"quality_controlled":"1","acknowledgement":"We thank Marco Mondelli for discussions related to LDPC decoding, and Giorgi Nadiradze for discussions on analysis of relaxed schedulers. This project has received funding from the European Research Council (ERC) under the European\r\nUnion’s Horizon 2020 research and innovation programme (grant agreement No 805223 ScaleML).","external_id":{"arxiv":["2002.11505"]},"month":"12","project":[{"grant_number":"805223","name":"Elastic Coordination for Scalable Machine Learning","call_identifier":"H2020","_id":"268A44D6-B435-11E9-9278-68D0E5697425"}],"corr_author":"1","ec_funded":1,"main_file_link":[{"url":"https://proceedings.neurips.cc/paper/2020/hash/fdb2c3bab9d0701c4a050a4d8d782c7f-Abstract.html","open_access":"1"}],"alternative_title":["Advances in Neural Information Processing Systems"],"title":"Scalable belief propagation via relaxed scheduling","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","conference":{"location":"Vancouver, Canada","start_date":"2020-12-06","name":"NeurIPS: Conference on Neural Information Processing Systems","end_date":"2020-12-12"},"scopus_import":"1","article_processing_charge":"No","year":"2020","intvolume":" 33","author":[{"full_name":"Aksenov, Vitaly","first_name":"Vitaly","last_name":"Aksenov"},{"full_name":"Alistarh, Dan-Adrian","id":"4A899BFC-F248-11E8-B48F-1D18A9856A87","first_name":"Dan-Adrian","orcid":"0000-0003-3650-940X","last_name":"Alistarh"},{"first_name":"Janne","last_name":"Korhonen","full_name":"Korhonen, Janne","id":"C5402D42-15BC-11E9-A202-CA2BE6697425"}],"abstract":[{"lang":"eng","text":"The ability to leverage large-scale hardware parallelism has been one of the key enablers of the accelerated recent progress in machine learning. Consequently, there has been considerable effort invested into developing efficient parallel variants of classic machine learning algorithms. However, despite the wealth of knowledge on parallelization, some classic machine learning algorithms often prove hard to parallelize efficiently while maintaining convergence. In this paper, we focus on efficient parallel algorithms for the key machine learning task of inference on graphical models, in particular on the fundamental belief propagation algorithm. We address the challenge of efficiently parallelizing this classic paradigm by showing how to leverage scalable relaxed schedulers in this context. We present an extensive empirical study, showing that our approach outperforms previous parallel belief propagation implementations both in terms of scalability and in terms of wall-clock convergence time, on a range of practical applications."}],"_id":"9631","date_updated":"2025-05-14T11:27:33Z","page":"22361-22372","department":[{"_id":"DaAl"}],"citation":{"mla":"Aksenov, Vitaly, et al. Scalable Belief Propagation via Relaxed Scheduling. Vol. 33, Neural Information Processing Systems Foundation, 2020, pp. 22361–72.","short":"V. Aksenov, D.-A. Alistarh, J. Korhonen, in:, Neural Information Processing Systems Foundation, 2020, pp. 22361–22372.","apa":"Aksenov, V., Alistarh, D.-A., & Korhonen, J. (2020). Scalable belief propagation via relaxed scheduling (Vol. 33, pp. 22361–22372). Presented at the NeurIPS: Conference on Neural Information Processing Systems, Vancouver, Canada: Neural Information Processing Systems Foundation.","ista":"Aksenov V, Alistarh D-A, Korhonen J. 2020. Scalable belief propagation via relaxed scheduling. NeurIPS: Conference on Neural Information Processing Systems, Advances in Neural Information Processing Systems, vol. 33, 22361–22372.","chicago":"Aksenov, Vitaly, Dan-Adrian Alistarh, and Janne Korhonen. “Scalable Belief Propagation via Relaxed Scheduling,” 33:22361–72. Neural Information Processing Systems Foundation, 2020.","ama":"Aksenov V, Alistarh D-A, Korhonen J. Scalable belief propagation via relaxed scheduling. In: Vol 33. Neural Information Processing Systems Foundation; 2020:22361-22372.","ieee":"V. Aksenov, D.-A. Alistarh, and J. Korhonen, “Scalable belief propagation via relaxed scheduling,” presented at the NeurIPS: Conference on Neural Information Processing Systems, Vancouver, Canada, 2020, vol. 33, pp. 22361–22372."},"date_published":"2020-12-06T00:00:00Z","language":[{"iso":"eng"}],"oa_version":"Published Version","date_created":"2021-07-04T22:01:26Z","type":"conference","oa":1},{"month":"12","project":[{"call_identifier":"H2020","grant_number":"805223","name":"Elastic Coordination for Scalable Machine Learning","_id":"268A44D6-B435-11E9-9278-68D0E5697425"}],"corr_author":"1","ec_funded":1,"main_file_link":[{"open_access":"1","url":"https://proceedings.neurips.cc/paper/2020/hash/d1ff1ec86b62cd5f3903ff19c3a326b2-Abstract.html"}],"publisher":"Neural Information Processing Systems Foundation","publication_status":"published","volume":33,"arxiv":1,"publication_identifier":{"isbn":["9781713829546"],"issn":["1049-5258"]},"day":"06","status":"public","external_id":{"arxiv":["2004.14340"]},"acknowledgement":"This project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 805223 ScaleML). Also, we would like to thank Alexander Shevchenko, Alexandra Peste, and other members of the group for fruitful discussions.","quality_controlled":"1","language":[{"iso":"eng"}],"page":"18098-18109","citation":{"ieee":"S. P. Singh and D.-A. Alistarh, “WoodFisher: Efficient second-order approximation for neural network compression,” presented at the NeurIPS: Conference on Neural Information Processing Systems, Vancouver, Canada, 2020, vol. 33, pp. 18098–18109.","ama":"Singh SP, Alistarh D-A. WoodFisher: Efficient second-order approximation for neural network compression. In: Vol 33. Neural Information Processing Systems Foundation; 2020:18098-18109.","ista":"Singh SP, Alistarh D-A. 2020. WoodFisher: Efficient second-order approximation for neural network compression. NeurIPS: Conference on Neural Information Processing Systems, Advances in Neural Information Processing Systems, vol. 33, 18098–18109.","chicago":"Singh, Sidak Pal, and Dan-Adrian Alistarh. “WoodFisher: Efficient Second-Order Approximation for Neural Network Compression,” 33:18098–109. Neural Information Processing Systems Foundation, 2020.","short":"S.P. Singh, D.-A. Alistarh, in:, Neural Information Processing Systems Foundation, 2020, pp. 18098–18109.","mla":"Singh, Sidak Pal, and Dan-Adrian Alistarh. WoodFisher: Efficient Second-Order Approximation for Neural Network Compression. Vol. 33, Neural Information Processing Systems Foundation, 2020, pp. 18098–109.","apa":"Singh, S. P., & Alistarh, D.-A. (2020). WoodFisher: Efficient second-order approximation for neural network compression (Vol. 33, pp. 18098–18109). Presented at the NeurIPS: Conference on Neural Information Processing Systems, Vancouver, Canada: Neural Information Processing Systems Foundation."},"date_published":"2020-12-06T00:00:00Z","department":[{"_id":"DaAl"},{"_id":"ToHe"}],"date_updated":"2025-05-14T11:27:23Z","_id":"9632","abstract":[{"text":"Second-order information, in the form of Hessian- or Inverse-Hessian-vector products, is a fundamental tool for solving optimization problems. Recently, there has been significant interest in utilizing this information in the context of deep\r\nneural networks; however, relatively little is known about the quality of existing approximations in this context. Our work examines this question, identifies issues with existing approaches, and proposes a method called WoodFisher to compute a faithful and efficient estimate of the inverse Hessian. Our main application is to neural network compression, where we build on the classic Optimal Brain Damage/Surgeon framework. We demonstrate that WoodFisher significantly outperforms popular state-of-the-art methods for oneshot pruning. Further, even when iterative, gradual pruning is allowed, our method results in a gain in test accuracy over the state-of-the-art approaches, for standard image classification datasets such as ImageNet ILSVRC. We examine how our method can be extended to take into account first-order information, as well as\r\nillustrate its ability to automatically set layer-wise pruning thresholds and perform compression in the limited-data regime. The code is available at the following link, https://github.com/IST-DASLab/WoodFisher.","lang":"eng"}],"type":"conference","oa_version":"Published Version","date_created":"2021-07-04T22:01:26Z","oa":1,"alternative_title":["Advances in Neural Information Processing Systems"],"scopus_import":"1","conference":{"location":"Vancouver, Canada","start_date":"2020-12-06","name":"NeurIPS: Conference on Neural Information Processing Systems","end_date":"2020-12-12"},"title":"WoodFisher: Efficient second-order approximation for neural network compression","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","year":"2020","article_processing_charge":"No","intvolume":" 33","author":[{"last_name":"Singh","first_name":"Sidak Pal","full_name":"Singh, Sidak Pal","id":"DD138E24-D89D-11E9-9DC0-DEF6E5697425"},{"full_name":"Alistarh, Dan-Adrian","id":"4A899BFC-F248-11E8-B48F-1D18A9856A87","first_name":"Dan-Adrian","orcid":"0000-0003-3650-940X","last_name":"Alistarh"}]},{"user_id":"6785fbc1-c503-11eb-8a32-93094b40e1cf","title":"Classical nucleation theory predicts the shape of the nucleus in homogeneous solidification","scopus_import":"1","year":"2020","article_processing_charge":"No","extern":"1","author":[{"full_name":"Cheng, Bingqing","id":"cbe3cda4-d82c-11eb-8dc7-8ff94289fcc9","orcid":"0000-0002-3584-9632","last_name":"Cheng","first_name":"Bingqing"},{"full_name":"Ceriotti, Michele","last_name":"Ceriotti","first_name":"Michele"},{"last_name":"Tribello","first_name":"Gareth A.","full_name":"Tribello, Gareth A."}],"intvolume":" 152","article_type":"original","citation":{"mla":"Cheng, Bingqing, et al. “Classical Nucleation Theory Predicts the Shape of the Nucleus in Homogeneous Solidification.” The Journal of Chemical Physics, vol. 152, no. 4, 044103, AIP Publishing, 2020, doi:10.1063/1.5134461.","short":"B. Cheng, M. Ceriotti, G.A. Tribello, The Journal of Chemical Physics 152 (2020).","apa":"Cheng, B., Ceriotti, M., & Tribello, G. A. (2020). Classical nucleation theory predicts the shape of the nucleus in homogeneous solidification. The Journal of Chemical Physics. AIP Publishing. https://doi.org/10.1063/1.5134461","ista":"Cheng B, Ceriotti M, Tribello GA. 2020. Classical nucleation theory predicts the shape of the nucleus in homogeneous solidification. The Journal of Chemical Physics. 152(4), 044103.","chicago":"Cheng, Bingqing, Michele Ceriotti, and Gareth A. Tribello. “Classical Nucleation Theory Predicts the Shape of the Nucleus in Homogeneous Solidification.” The Journal of Chemical Physics. AIP Publishing, 2020. https://doi.org/10.1063/1.5134461.","ama":"Cheng B, Ceriotti M, Tribello GA. Classical nucleation theory predicts the shape of the nucleus in homogeneous solidification. The Journal of Chemical Physics. 2020;152(4). doi:10.1063/1.5134461","ieee":"B. Cheng, M. Ceriotti, and G. A. Tribello, “Classical nucleation theory predicts the shape of the nucleus in homogeneous solidification,” The Journal of Chemical Physics, vol. 152, no. 4. AIP Publishing, 2020."},"date_published":"2020-01-31T00:00:00Z","abstract":[{"lang":"eng","text":"Macroscopic models of nucleation provide powerful tools for understanding activated phase transition processes. These models do not provide atomistic insights and can thus sometimes lack material-specific descriptions. Here, we provide a comprehensive framework for constructing a continuum picture from an atomistic simulation of homogeneous nucleation. We use this framework to determine the equilibrium shape of the solid nucleus that forms inside bulk liquid for a Lennard-Jones potential. From this shape, we then extract the anisotropy of the solid-liquid interfacial free energy, by performing a reverse Wulff construction in the space of spherical harmonic expansions. We find that the shape of the nucleus is nearly spherical and that its anisotropy can be perfectly described using classical models."}],"_id":"9658","date_updated":"2023-02-23T14:03:55Z","language":[{"iso":"eng"}],"oa_version":"Submitted Version","date_created":"2021-07-15T07:22:24Z","type":"journal_article","article_number":"044103","issue":"4","oa":1,"publication":"The Journal of Chemical Physics","volume":152,"publisher":"AIP Publishing","publication_status":"published","pmid":1,"day":"31","status":"public","arxiv":1,"publication_identifier":{"issn":["0021-9606"],"eissn":["1089-7690"]},"quality_controlled":"1","external_id":{"pmid":["32007057"],"arxiv":["1910.13481"]},"month":"01","doi":"10.1063/1.5134461","main_file_link":[{"url":"https://pure.qub.ac.uk/en/publications/classical-nucleation-theory-predicts-the-shape-of-the-nucleus-in-homogeneous-solidification(56af848b-eee8-4e9b-93cf-667373e4a49b).html","open_access":"1"}]},{"doi":"10.1103/physrevlett.125.130602","month":"09","main_file_link":[{"url":"https://arxiv.org/abs/2005.07562","open_access":"1"}],"day":"25","pmid":1,"status":"public","arxiv":1,"publication_identifier":{"issn":["0031-9007"],"eissn":["1079-7114"]},"publication":"Physical Review Letters","volume":125,"publisher":"American Physical Society","publication_status":"published","quality_controlled":"1","external_id":{"pmid":["33034481"],"arxiv":["2005.07562"]},"date_published":"2020-09-25T00:00:00Z","citation":{"ama":"Cheng B, Frenkel D. Computing the heat conductivity of fluids from density fluctuations. Physical Review Letters. 2020;125(13). doi:10.1103/physrevlett.125.130602","ieee":"B. Cheng and D. Frenkel, “Computing the heat conductivity of fluids from density fluctuations,” Physical Review Letters, vol. 125, no. 13. American Physical Society, 2020.","ista":"Cheng B, Frenkel D. 2020. Computing the heat conductivity of fluids from density fluctuations. Physical Review Letters. 125(13), 130602.","chicago":"Cheng, Bingqing, and Daan Frenkel. “Computing the Heat Conductivity of Fluids from Density Fluctuations.” Physical Review Letters. American Physical Society, 2020. https://doi.org/10.1103/physrevlett.125.130602.","mla":"Cheng, Bingqing, and Daan Frenkel. “Computing the Heat Conductivity of Fluids from Density Fluctuations.” Physical Review Letters, vol. 125, no. 13, 130602, American Physical Society, 2020, doi:10.1103/physrevlett.125.130602.","short":"B. Cheng, D. Frenkel, Physical Review Letters 125 (2020).","apa":"Cheng, B., & Frenkel, D. (2020). Computing the heat conductivity of fluids from density fluctuations. Physical Review Letters. American Physical Society. https://doi.org/10.1103/physrevlett.125.130602"},"_id":"9664","abstract":[{"lang":"eng","text":"Equilibrium molecular dynamics simulations, in combination with the Green-Kubo (GK) method, have been extensively used to compute the thermal conductivity of liquids. However, the GK method relies on an ambiguous definition of the microscopic heat flux, which depends on how one chooses to distribute energies over atoms. This ambiguity makes it problematic to employ the GK method for systems with nonpairwise interactions. In this work, we show that the hydrodynamic description of thermally driven density fluctuations can be used to obtain the thermal conductivity of a bulk fluid unambiguously, thereby bypassing the need to define the heat flux. We verify that, for a model fluid with only pairwise interactions, our method yields estimates of thermal conductivity consistent with the GK approach. We apply our approach to compute the thermal conductivity of a nonpairwise additive water model at supercritical conditions, and of a liquid hydrogen system described by a machine-learning interatomic potential, at 33 GPa and 2000 K."}],"date_updated":"2021-08-09T12:35:58Z","language":[{"iso":"eng"}],"issue":"13","oa":1,"oa_version":"Preprint","date_created":"2021-07-15T12:15:14Z","type":"journal_article","article_number":"130602","user_id":"6785fbc1-c503-11eb-8a32-93094b40e1cf","title":"Computing the heat conductivity of fluids from density fluctuations","scopus_import":"1","intvolume":" 125","author":[{"id":"cbe3cda4-d82c-11eb-8dc7-8ff94289fcc9","full_name":"Cheng, Bingqing","last_name":"Cheng","orcid":"0000-0002-3584-9632","first_name":"Bingqing"},{"first_name":"Daan","last_name":"Frenkel","full_name":"Frenkel, Daan"}],"article_type":"original","year":"2020","article_processing_charge":"No","extern":"1"},{"date_created":"2021-07-15T12:37:27Z","oa_version":"Published Version","type":"journal_article","issue":"22","oa":1,"date_published":"2020-06-14T00:00:00Z","citation":{"ieee":"A. Reinhardt, C. J. Pickard, and B. Cheng, “Predicting the phase diagram of titanium dioxide with random search and pattern recognition,” Physical Chemistry Chemical Physics, vol. 22, no. 22. Royal Society of Chemistry, pp. 12697–12705, 2020.","ama":"Reinhardt A, Pickard CJ, Cheng B. Predicting the phase diagram of titanium dioxide with random search and pattern recognition. Physical Chemistry Chemical Physics. 2020;22(22):12697-12705. doi:10.1039/d0cp02513e","chicago":"Reinhardt, Aleks, Chris J. Pickard, and Bingqing Cheng. “Predicting the Phase Diagram of Titanium Dioxide with Random Search and Pattern Recognition.” Physical Chemistry Chemical Physics. Royal Society of Chemistry, 2020. https://doi.org/10.1039/d0cp02513e.","ista":"Reinhardt A, Pickard CJ, Cheng B. 2020. Predicting the phase diagram of titanium dioxide with random search and pattern recognition. Physical Chemistry Chemical Physics. 22(22), 12697–12705.","apa":"Reinhardt, A., Pickard, C. J., & Cheng, B. (2020). Predicting the phase diagram of titanium dioxide with random search and pattern recognition. Physical Chemistry Chemical Physics. Royal Society of Chemistry. https://doi.org/10.1039/d0cp02513e","mla":"Reinhardt, Aleks, et al. “Predicting the Phase Diagram of Titanium Dioxide with Random Search and Pattern Recognition.” Physical Chemistry Chemical Physics, vol. 22, no. 22, Royal Society of Chemistry, 2020, pp. 12697–705, doi:10.1039/d0cp02513e.","short":"A. Reinhardt, C.J. Pickard, B. Cheng, Physical Chemistry Chemical Physics 22 (2020) 12697–12705."},"page":"12697-12705","abstract":[{"text":"Predicting phase stabilities of crystal polymorphs is central to computational materials science and chemistry. Such predictions are challenging because they first require searching for potential energy minima and then performing arduous free-energy calculations to account for entropic effects at finite temperatures. Here, we develop a framework that facilitates such predictions by exploiting all the information obtained from random searches of crystal structures. This framework combines automated clustering, classification and visualisation of crystal structures with machine-learning estimation of their enthalpy and entropy. We demonstrate the framework on the technologically important system of TiO2, which has many polymorphs, without relying on prior knowledge of known phases. We find a number of new phases and predict the phase diagram and metastabilities of crystal polymorphs at 1600 K, benchmarking the results against full free-energy calculations.","lang":"eng"}],"_id":"9666","date_updated":"2024-10-16T12:29:54Z","has_accepted_license":"1","language":[{"iso":"eng"}],"year":"2020","article_processing_charge":"No","extern":"1","intvolume":" 22","author":[{"first_name":"Aleks","last_name":"Reinhardt","full_name":"Reinhardt, Aleks"},{"first_name":"Chris J.","last_name":"Pickard","full_name":"Pickard, Chris J."},{"full_name":"Cheng, Bingqing","id":"cbe3cda4-d82c-11eb-8dc7-8ff94289fcc9","first_name":"Bingqing","last_name":"Cheng","orcid":"0000-0002-3584-9632"}],"article_type":"original","title":"Predicting the phase diagram of titanium dioxide with random search and pattern recognition","user_id":"0043cee0-e5fc-11ee-9736-f83bc23afbf0","scopus_import":"1","OA_place":"publisher","month":"06","license":"https://creativecommons.org/licenses/by/3.0/","doi":"10.1039/d0cp02513e","file_date_updated":"2021-07-15T12:43:51Z","OA_type":"hybrid","quality_controlled":"1","ddc":["530"],"external_id":{"pmid":["32459228"],"arxiv":["1909.08934"]},"publication":"Physical Chemistry Chemical Physics","volume":22,"tmp":{"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)","short":"CC BY (3.0)"},"publisher":"Royal Society of Chemistry","publication_status":"published","day":"14","pmid":1,"status":"public","file":[{"file_id":"9667","relation":"main_file","date_created":"2021-07-15T12:43:51Z","file_size":3151206,"success":1,"file_name":"202_PhysicalChemistryChemicalPhysics_Reinhardt.pdf","access_level":"open_access","creator":"asandaue","date_updated":"2021-07-15T12:43:51Z","checksum":"0a6872972b1b2e60f9095d39b01753fa","content_type":"application/pdf"}],"arxiv":1,"publication_identifier":{"eissn":["1463-9084"],"issn":["1463-9076"]}},{"quality_controlled":"1","ddc":["530","540"],"tmp":{"legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","image":"/images/cc_by.png","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","short":"CC BY (4.0)"},"volume":11,"publication":"Nature Communications","publication_status":"published","publisher":"Springer Nature","status":"public","day":"13","publication_identifier":{"eissn":["2041-1723"]},"file":[{"file_id":"9672","relation":"main_file","date_created":"2021-07-15T14:05:45Z","file_size":1385954,"access_level":"open_access","file_name":"2020_NatureCommunications_Monserrat.pdf","success":1,"creator":"asandaue","date_updated":"2021-07-15T14:05:45Z","content_type":"application/pdf","checksum":"1edd9b6d8fa791f8094d87bd6453955b"}],"month":"11","file_date_updated":"2021-07-15T14:05:45Z","doi":"10.1038/s41467-020-19606-y","article_processing_charge":"No","year":"2020","extern":"1","author":[{"first_name":"Bartomeu","last_name":"Monserrat","full_name":"Monserrat, Bartomeu"},{"full_name":"Brandenburg, Jan Gerit","last_name":"Brandenburg","first_name":"Jan Gerit"},{"full_name":"Engel, Edgar A.","last_name":"Engel","first_name":"Edgar A."},{"full_name":"Cheng, Bingqing","id":"cbe3cda4-d82c-11eb-8dc7-8ff94289fcc9","last_name":"Cheng","orcid":"0000-0002-3584-9632","first_name":"Bingqing"}],"intvolume":" 11","article_type":"original","user_id":"6785fbc1-c503-11eb-8a32-93094b40e1cf","title":"Liquid water contains the building blocks of diverse ice phases","scopus_import":"1","date_created":"2021-07-15T14:01:35Z","oa_version":"Published Version","article_number":"5757","type":"journal_article","issue":"1","oa":1,"_id":"9671","date_updated":"2023-02-23T14:04:25Z","abstract":[{"lang":"eng","text":"Water molecules can arrange into a liquid with complex hydrogen-bond networks and at least 17 experimentally confirmed ice phases with enormous structural diversity. It remains a puzzle how or whether this multitude of arrangements in different phases of water are related. Here we investigate the structural similarities between liquid water and a comprehensive set of 54 ice phases in simulations, by directly comparing their local environments using general atomic descriptors, and also by demonstrating that a machine-learning potential trained on liquid water alone can predict the densities, lattice energies, and vibrational properties of the ices. The finding that the local environments characterising the different ice phases are found in water sheds light on the phase behavior of water, and rationalizes the transferability of water models between different phases."}],"citation":{"mla":"Monserrat, Bartomeu, et al. “Liquid Water Contains the Building Blocks of Diverse Ice Phases.” Nature Communications, vol. 11, no. 1, 5757, Springer Nature, 2020, doi:10.1038/s41467-020-19606-y.","short":"B. Monserrat, J.G. Brandenburg, E.A. Engel, B. Cheng, Nature Communications 11 (2020).","apa":"Monserrat, B., Brandenburg, J. G., Engel, E. A., & Cheng, B. (2020). Liquid water contains the building blocks of diverse ice phases. Nature Communications. Springer Nature. https://doi.org/10.1038/s41467-020-19606-y","ista":"Monserrat B, Brandenburg JG, Engel EA, Cheng B. 2020. Liquid water contains the building blocks of diverse ice phases. Nature Communications. 11(1), 5757.","chicago":"Monserrat, Bartomeu, Jan Gerit Brandenburg, Edgar A. Engel, and Bingqing Cheng. “Liquid Water Contains the Building Blocks of Diverse Ice Phases.” Nature Communications. Springer Nature, 2020. https://doi.org/10.1038/s41467-020-19606-y.","ieee":"B. Monserrat, J. G. Brandenburg, E. A. Engel, and B. Cheng, “Liquid water contains the building blocks of diverse ice phases,” Nature Communications, vol. 11, no. 1. Springer Nature, 2020.","ama":"Monserrat B, Brandenburg JG, Engel EA, Cheng B. Liquid water contains the building blocks of diverse ice phases. Nature Communications. 2020;11(1). doi:10.1038/s41467-020-19606-y"},"date_published":"2020-11-13T00:00:00Z","language":[{"iso":"eng"}],"has_accepted_license":"1"},{"date_published":"2020-08-14T00:00:00Z","citation":{"chicago":"Cheng, Bingqing, Ryan-Rhys Griffiths, Simon Wengert, Christian Kunkel, Tamas Stenczel, Bonan Zhu, Volker L. Deringer, et al. “Mapping Materials and Molecules.” Accounts of Chemical Research. American Chemical Society, 2020. https://doi.org/10.1021/acs.accounts.0c00403.","ista":"Cheng B, Griffiths R-R, Wengert S, Kunkel C, Stenczel T, Zhu B, Deringer VL, Bernstein N, Margraf JT, Reuter K, Csanyi G. 2020. Mapping materials and molecules. Accounts of Chemical Research. 53(9), 1981–1991.","ama":"Cheng B, Griffiths R-R, Wengert S, et al. Mapping materials and molecules. Accounts of Chemical Research. 2020;53(9):1981-1991. doi:10.1021/acs.accounts.0c00403","ieee":"B. Cheng et al., “Mapping materials and molecules,” Accounts of Chemical Research, vol. 53, no. 9. American Chemical Society, pp. 1981–1991, 2020.","apa":"Cheng, B., Griffiths, R.-R., Wengert, S., Kunkel, C., Stenczel, T., Zhu, B., … Csanyi, G. (2020). Mapping materials and molecules. Accounts of Chemical Research. American Chemical Society. https://doi.org/10.1021/acs.accounts.0c00403","short":"B. Cheng, R.-R. Griffiths, S. Wengert, C. Kunkel, T. Stenczel, B. Zhu, V.L. Deringer, N. Bernstein, J.T. Margraf, K. Reuter, G. Csanyi, Accounts of Chemical Research 53 (2020) 1981–1991.","mla":"Cheng, Bingqing, et al. “Mapping Materials and Molecules.” Accounts of Chemical Research, vol. 53, no. 9, American Chemical Society, 2020, pp. 1981–91, doi:10.1021/acs.accounts.0c00403."},"page":"1981-1991","_id":"9675","abstract":[{"text":"The visualization of data is indispensable in scientific research, from the early stages when human insight forms to the final step of communicating results. In computational physics, chemistry and materials science, it can be as simple as making a scatter plot or as straightforward as looking through the snapshots of atomic positions manually. However, as a result of the \"big data\" revolution, these conventional approaches are often inadequate. The widespread adoption of high-throughput computation for materials discovery and the associated community-wide repositories have given rise to data sets that contain an enormous number of compounds and atomic configurations. A typical data set contains thousands to millions of atomic structures, along with a diverse range of properties such as formation energies, band gaps, or bioactivities.It would thus be desirable to have a data-driven and automated framework for visualizing and analyzing such structural data sets. The key idea is to construct a low-dimensional representation of the data, which facilitates navigation, reveals underlying patterns, and helps to identify data points with unusual attributes. Such data-intensive maps, often employing machine learning methods, are appearing more and more frequently in the literature. However, to the wider community, it is not always transparent how these maps are made and how they should be interpreted. Furthermore, while these maps undoubtedly serve a decorative purpose in academic publications, it is not always apparent what extra information can be garnered from reading or making them.This Account attempts to answer such questions. We start with a concise summary of the theory of representing chemical environments, followed by the introduction of a simple yet practical conceptual approach for generating structure maps in a generic and automated manner. Such analysis and mapping is made nearly effortless by employing the newly developed software tool ASAP. To showcase the applicability to a wide variety of systems in chemistry and materials science, we provide several illustrative examples, including crystalline and amorphous materials, interfaces, and organic molecules. In these examples, the maps not only help to sift through large data sets but also reveal hidden patterns that could be easily missed using conventional analyses.The explosion in the amount of computed information in chemistry and materials science has made visualization into a science in itself. Not only have we benefited from exploiting these visualization methods in previous works, we also believe that the automated mapping of data sets will in turn stimulate further creativity and exploration, as well as ultimately feed back into future advances in the respective fields.","lang":"eng"}],"date_updated":"2021-11-24T15:54:41Z","language":[{"iso":"eng"}],"issue":"9","oa_version":"None","date_created":"2021-07-16T06:25:53Z","type":"journal_article","title":"Mapping materials and molecules","user_id":"8b945eb4-e2f2-11eb-945a-df72226e66a9","scopus_import":"1","author":[{"full_name":"Cheng, Bingqing","id":"cbe3cda4-d82c-11eb-8dc7-8ff94289fcc9","last_name":"Cheng","orcid":"0000-0002-3584-9632","first_name":"Bingqing"},{"full_name":"Griffiths, Ryan-Rhys","first_name":"Ryan-Rhys","last_name":"Griffiths"},{"first_name":"Simon","last_name":"Wengert","full_name":"Wengert, Simon"},{"first_name":"Christian","last_name":"Kunkel","full_name":"Kunkel, Christian"},{"last_name":"Stenczel","first_name":"Tamas","full_name":"Stenczel, Tamas"},{"full_name":"Zhu, Bonan","first_name":"Bonan","last_name":"Zhu"},{"full_name":"Deringer, Volker L.","last_name":"Deringer","first_name":"Volker L."},{"first_name":"Noam","last_name":"Bernstein","full_name":"Bernstein, Noam"},{"full_name":"Margraf, Johannes T.","last_name":"Margraf","first_name":"Johannes T."},{"full_name":"Reuter, Karsten","last_name":"Reuter","first_name":"Karsten"},{"first_name":"Gabor","last_name":"Csanyi","full_name":"Csanyi, Gabor"}],"intvolume":" 53","article_type":"original","year":"2020","article_processing_charge":"No","extern":"1","doi":"10.1021/acs.accounts.0c00403","month":"08","day":"14","pmid":1,"status":"public","publication_identifier":{"issn":["0001-4842"],"eissn":["1520-4898"]},"publication":"Accounts of Chemical Research","volume":53,"publisher":"American Chemical Society","publication_status":"published","quality_controlled":"1","external_id":{"pmid":["32794697"]}},{"author":[{"orcid":"0000-0002-3584-9632","last_name":"Cheng","first_name":"Bingqing","full_name":"Cheng, Bingqing","id":"cbe3cda4-d82c-11eb-8dc7-8ff94289fcc9"},{"full_name":"Mazzola, Guglielmo","first_name":"Guglielmo","last_name":"Mazzola"},{"last_name":"Pickard","first_name":"Chris J.","full_name":"Pickard, Chris J."},{"last_name":"Ceriotti","first_name":"Michele","full_name":"Ceriotti, Michele"}],"intvolume":" 585","article_type":"original","year":"2020","article_processing_charge":"No","extern":"1","user_id":"6785fbc1-c503-11eb-8a32-93094b40e1cf","title":"Evidence for supercritical behaviour of high-pressure liquid hydrogen","scopus_import":"1","issue":"7824","oa":1,"date_created":"2021-07-19T09:17:49Z","oa_version":"Preprint","type":"journal_article","page":"217-220","date_published":"2020-09-10T00:00:00Z","citation":{"apa":"Cheng, B., Mazzola, G., Pickard, C. J., & Ceriotti, M. (2020). Evidence for supercritical behaviour of high-pressure liquid hydrogen. Nature. Springer Nature. https://doi.org/10.1038/s41586-020-2677-y","mla":"Cheng, Bingqing, et al. “Evidence for Supercritical Behaviour of High-Pressure Liquid Hydrogen.” Nature, vol. 585, no. 7824, Springer Nature, 2020, pp. 217–20, doi:10.1038/s41586-020-2677-y.","short":"B. Cheng, G. Mazzola, C.J. Pickard, M. Ceriotti, Nature 585 (2020) 217–220.","ama":"Cheng B, Mazzola G, Pickard CJ, Ceriotti M. Evidence for supercritical behaviour of high-pressure liquid hydrogen. Nature. 2020;585(7824):217-220. doi:10.1038/s41586-020-2677-y","ieee":"B. Cheng, G. Mazzola, C. J. Pickard, and M. Ceriotti, “Evidence for supercritical behaviour of high-pressure liquid hydrogen,” Nature, vol. 585, no. 7824. Springer Nature, pp. 217–220, 2020.","chicago":"Cheng, Bingqing, Guglielmo Mazzola, Chris J. Pickard, and Michele Ceriotti. “Evidence for Supercritical Behaviour of High-Pressure Liquid Hydrogen.” Nature. Springer Nature, 2020. https://doi.org/10.1038/s41586-020-2677-y.","ista":"Cheng B, Mazzola G, Pickard CJ, Ceriotti M. 2020. Evidence for supercritical behaviour of high-pressure liquid hydrogen. Nature. 585(7824), 217–220."},"_id":"9685","date_updated":"2021-08-09T12:38:01Z","abstract":[{"text":"Hydrogen, the simplest and most abundant element in the Universe, develops a remarkably complex behaviour upon compression^1. Since Wigner predicted the dissociation and metallization of solid hydrogen at megabar pressures almost a century ago^2, several efforts have been made to explain the many unusual properties of dense hydrogen, including a rich and poorly understood solid polymorphism^1,3-5, an anomalous melting line6 and the possible transition to a superconducting state^7. Experiments at such extreme conditions are challenging and often lead to hard-to-interpret and controversial observations, whereas theoretical investigations are constrained by the huge computational cost of sufficiently accurate quantum mechanical calculations. Here we present a theoretical study of the phase diagram of dense hydrogen that uses machine learning to 'learn' potential-energy surfaces and interatomic forces from reference calculations and then predict them at low computational cost, overcoming length- and timescale limitations. We reproduce both the re-entrant melting behaviour and the polymorphism of the solid phase. Simulations using our machine-learning-based potentials provide evidence for a continuous molecular-to-atomic transition in the liquid, with no first-order transition observed above the melting line. This suggests a smooth transition between insulating and metallic layers in giant gas planets, and reconciles existing discrepancies between experiments as a manifestation of supercritical behaviour.","lang":"eng"}],"language":[{"iso":"eng"}],"quality_controlled":"1","external_id":{"arxiv":["1906.03341"],"pmid":["32908269"]},"pmid":1,"day":"10","status":"public","arxiv":1,"publication_identifier":{"eissn":["1476-4687"],"issn":["0028-0836"]},"publication":"Nature","volume":585,"publisher":"Springer Nature","publication_status":"published","main_file_link":[{"open_access":"1","url":"https://arxiv.org/abs/1906.03341"}],"doi":"10.1038/s41586-020-2677-y","month":"09"},{"status":"public","title":"Extracting ice phases from liquid water: Why a machine-learning water model generalizes so well","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","day":"23","arxiv":1,"publication":"arXiv","publication_status":"submitted","author":[{"first_name":"Bartomeu","last_name":"Monserrat","full_name":"Monserrat, Bartomeu"},{"full_name":"Brandenburg, Jan Gerit","last_name":"Brandenburg","first_name":"Jan Gerit"},{"full_name":"Engel, Edgar A.","last_name":"Engel","first_name":"Edgar A."},{"first_name":"Bingqing","orcid":"0000-0002-3584-9632","last_name":"Cheng","id":"cbe3cda4-d82c-11eb-8dc7-8ff94289fcc9","full_name":"Cheng, Bingqing"}],"external_id":{"arxiv":["2006.13316"]},"article_processing_charge":"No","year":"2020","extern":"1","doi":"10.48550/arXiv.2006.13316","_id":"9699","date_updated":"2024-10-14T12:05:56Z","abstract":[{"text":"We investigate the structural similarities between liquid water and 53 ices, including 20 known crystalline phases. We base such similarity comparison on the local environments that consist of atoms within a certain cutoff radius of a central atom. We reveal that liquid water explores the local environments of the diverse ice phases, by directly comparing the environments in these phases using general atomic descriptors, and also by demonstrating that a machine-learning potential trained on liquid water alone can predict the densities, the lattice energies, and vibrational properties of the\r\nices. The finding that the local environments characterising the different ice phases are found in water sheds light on water phase behaviors, and rationalizes the transferability of water models between different phases.","lang":"eng"}],"date_published":"2020-06-23T00:00:00Z","citation":{"ista":"Monserrat B, Brandenburg JG, Engel EA, Cheng B. Extracting ice phases from liquid water: Why a machine-learning water model generalizes so well. arXiv, 2006.13316.","chicago":"Monserrat, Bartomeu, Jan Gerit Brandenburg, Edgar A. Engel, and Bingqing Cheng. “Extracting Ice Phases from Liquid Water: Why a Machine-Learning Water Model Generalizes so Well.” ArXiv, n.d. https://doi.org/10.48550/arXiv.2006.13316.","ieee":"B. Monserrat, J. G. Brandenburg, E. A. Engel, and B. Cheng, “Extracting ice phases from liquid water: Why a machine-learning water model generalizes so well,” arXiv. .","ama":"Monserrat B, Brandenburg JG, Engel EA, Cheng B. Extracting ice phases from liquid water: Why a machine-learning water model generalizes so well. arXiv. doi:10.48550/arXiv.2006.13316","short":"B. Monserrat, J.G. Brandenburg, E.A. Engel, B. Cheng, ArXiv (n.d.).","mla":"Monserrat, Bartomeu, et al. “Extracting Ice Phases from Liquid Water: Why a Machine-Learning Water Model Generalizes so Well.” ArXiv, 2006.13316, doi:10.48550/arXiv.2006.13316.","apa":"Monserrat, B., Brandenburg, J. G., Engel, E. A., & Cheng, B. (n.d.). Extracting ice phases from liquid water: Why a machine-learning water model generalizes so well. arXiv. https://doi.org/10.48550/arXiv.2006.13316"},"language":[{"iso":"eng"}],"month":"06","main_file_link":[{"open_access":"1","url":"https://arxiv.org/abs/2006.13316"}],"oa":1,"date_created":"2021-07-20T11:25:15Z","oa_version":"Submitted Version","article_number":"2006.13316","type":"preprint"}]