{"acknowledgement":"B. C. thanks David Limmer for providing the water slab dataset, and Carolin Faller for the NaCl dataset.","month":"03","corr_author":"1","publisher":"Springer Nature","status":"public","quality_controlled":"1","intvolume":"        11","publication_identifier":{"eissn":["2057-3960"]},"ddc":["000"],"DOAJ_listed":"1","volume":11,"publication_status":"published","arxiv":1,"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","has_accepted_license":"1","type":"journal_article","year":"2025","OA_type":"gold","day":"26","OA_place":"publisher","file_date_updated":"2025-04-08T09:34:58Z","date_updated":"2025-05-14T11:32:17Z","department":[{"_id":"BiCh"}],"language":[{"iso":"eng"}],"title":"Latent Ewald summation for machine learning of long-range interactions","scopus_import":"1","date_created":"2025-04-06T22:01:32Z","doi":"10.1038/s41524-025-01577-7","article_number":"80","date_published":"2025-03-26T00:00:00Z","oa":1,"license":"https://creativecommons.org/licenses/by/4.0/","external_id":{"arxiv":["2408.15165"]},"author":[{"first_name":"Bingqing","full_name":"Cheng, Bingqing","id":"cbe3cda4-d82c-11eb-8dc7-8ff94289fcc9","last_name":"Cheng","orcid":"0000-0002-3584-9632"}],"_id":"19495","file":[{"creator":"dernst","date_created":"2025-04-08T09:34:58Z","relation":"main_file","success":1,"date_updated":"2025-04-08T09:34:58Z","file_id":"19528","content_type":"application/pdf","access_level":"open_access","file_size":1608315,"checksum":"cc99b7407a12139d9b2d8457961935ae","file_name":"2025_npjCompMaterials_Cheng.pdf"}],"article_processing_charge":"Yes","publication":"npj Computational Materials","oa_version":"Published Version","article_type":"original","tmp":{"image":"/images/cc_by.png","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","short":"CC BY (4.0)"},"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."}],"citation":{"short":"B. Cheng, Npj Computational Materials 11 (2025).","ieee":"B. Cheng, “Latent Ewald summation for machine learning of long-range interactions,” <i>npj Computational Materials</i>, vol. 11. Springer Nature, 2025.","ista":"Cheng B. 2025. Latent Ewald summation for machine learning of long-range interactions. npj Computational Materials. 11, 80.","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>.","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>","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>","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>."}}