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Learn.: Sci. Technol."],"published-print":{"date-parts":[[2025,3,31]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Electrolyte solutions play critical role in a vast range of important applications, yet an accurate and scalable method of predicting their properties without fitting to experiment has remained out of reach, despite over a century of effort. Here, we combine state-of-the-art density functional theory and equivariant neural network potentials to demonstrate this capability, reproducing key structural, thermodynamic, and kinetic properties. We show that neural network potentials can be recursively trained on a subset of their own output to enable coarse-grained\/continuum-solvent molecular simulations that can access much longer timescales than possible with all atom simulations. We observe the surprising formation of Li cation dimers along with identical anion-anion pairing of chloride and bromide anions. Finally, we simulate the crystal phase and infinite dilution pairing free energies despite being trained only on moderate concentration solutions. This approach should be scaled to build a greatly expanded database of electrolyte solution properties than currently exists.<\/jats:p>","DOI":"10.1088\/2632-2153\/adaf76","type":"journal-article","created":{"date-parts":[[2025,1,28]],"date-time":"2025-01-28T22:58:45Z","timestamp":1738105125000},"page":"015053","update-policy":"https:\/\/doi.org\/10.1088\/crossmark-policy","source":"Crossref","is-referenced-by-count":4,"title":["Scalable and accurate simulation of electrolyte solutions with quantum chemical accuracy"],"prefix":"10.1088","volume":"6","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5066-6298","authenticated-orcid":true,"given":"Junji","family":"Zhang","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8103-053X","authenticated-orcid":false,"given":"Joshua","family":"Pagotto","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7191-9124","authenticated-orcid":false,"given":"Tim","family":"Gould","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3772-8057","authenticated-orcid":true,"given":"Timothy T","family":"Duignan","sequence":"additional","affiliation":[]}],"member":"266","published-online":{"date-parts":[[2025,2,26]]},"reference":[{"key":"mlstadaf76bib1","doi-asserted-by":"publisher","first-page":"59","DOI":"10.1038\/s41586-020-03072-z","article-title":"Origins of structural and electronic transitions in disordered silicon","volume":"589","author":"Deringer","year":"2021","journal-title":"Nature"},{"key":"mlstadaf76bib2","doi-asserted-by":"publisher","first-page":"921","DOI":"10.1126\/science.abd7716","article-title":"Reactive uptake of N2O5 by atmospheric aerosol is dominated by interfacial processes","volume":"371","author":"Galib","year":"2021","journal-title":"Science"},{"key":"mlstadaf76bib3","doi-asserted-by":"publisher","first-page":"512","DOI":"10.1038\/s41586-022-05036-x","article-title":"The first-principles phase diagram of monolayer nanoconfined water","volume":"609","author":"Kapil","year":"2022","journal-title":"Nature"},{"key":"mlstadaf76bib4","doi-asserted-by":"publisher","first-page":"3349","DOI":"10.1038\/s41467-023-38855-1","article-title":"Realistic phase diagram of water from \u201cfirst principles\u201d data-driven quantum simulations","volume":"14","author":"Bore","year":"2023","journal-title":"Nat. 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