{"abstract":[{"lang":"eng","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."}],"citation":{"short":"D. Kim, B. Cheng, The Journal of Chemical Physics 164 (2026).","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>","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>","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.","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.","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>."},"corr_author":"1","author":[{"first_name":"Dongjin","full_name":"Kim, Dongjin","last_name":"Kim"},{"last_name":"Cheng","id":"cbe3cda4-d82c-11eb-8dc7-8ff94289fcc9","first_name":"Bingqing","orcid":"0000-0002-3584-9632","full_name":"Cheng, Bingqing"}],"article_processing_charge":"No","quality_controlled":"1","oa_version":"Preprint","month":"02","article_type":"original","external_id":{"arxiv":["2512.18029"]},"title":"Long-range electrostatics for machine learning interatomic potentials is easier than we thought","date_published":"2026-02-14T00:00:00Z","article_number":"060901","publication":"The Journal of Chemical Physics","date_created":"2026-03-02T10:06:46Z","_id":"21381","publication_status":"published","OA_type":"free access","year":"2026","publication_identifier":{"issn":["0021-9606"],"eissn":["1089-7690"]},"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","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.","doi":"10.1063/5.0316886","language":[{"iso":"eng"}],"status":"public","volume":164,"date_updated":"2026-03-02T14:46:24Z","scopus_import":"1","publisher":"AIP Publishing","main_file_link":[{"open_access":"1","url":"https://doi.org/10.48550/arXiv.2512.18029"}],"issue":"6","oa":1,"intvolume":"       164","type":"journal_article","department":[{"_id":"BiCh"}],"day":"14","arxiv":1,"OA_place":"repository"}