{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,19]],"date-time":"2026-05-19T22:11:31Z","timestamp":1779228691884,"version":"3.51.4"},"reference-count":37,"publisher":"IOP Publishing","issue":"3","license":[{"start":{"date-parts":[[2023,8,21]],"date-time":"2023-08-21T00:00:00Z","timestamp":1692576000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2023,8,21]],"date-time":"2023-08-21T00:00:00Z","timestamp":1692576000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/iopscience.iop.org\/info\/page\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/100000015","name":"U.S. Department of Energy","doi-asserted-by":"crossref","award":["A053685"],"award-info":[{"award-number":["A053685"]}],"id":[{"id":"10.13039\/100000015","id-type":"DOI","asserted-by":"crossref"}]},{"name":"National Science Foundation","award":["Grant 1820747"],"award-info":[{"award-number":["Grant 1820747"]}]}],"content-domain":{"domain":["iopscience.iop.org"],"crossmark-restriction":false},"short-container-title":["Mach. Learn.: Sci. Technol."],"published-print":{"date-parts":[[2023,9,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>The construction of a better exchange-correlation potential in time-dependent density functional theory (TDDFT) can improve the accuracy of TDDFT calculations and provide more accurate predictions of the properties of many-electron systems. Here, we propose a machine learning method to develop the energy functional and the Kohn\u2013Sham potential of a time-dependent Kohn\u2013Sham (TDKS) system is proposed. The method is based on the dynamics of the Kohn\u2013Sham system and does not require any data on the exact Kohn\u2013Sham potential for training the model. We demonstrate the results of our method with a 1D harmonic oscillator example and a 1D two-electron example. We show that the machine-learned Kohn\u2013Sham potential matches the exact Kohn\u2013Sham potential in the absence of memory effect. Our method can still capture the dynamics of the Kohn\u2013Sham system in the presence of memory effects. The machine learning method developed in this article provides insight into making better approximations of the energy functional and the Kohn\u2013Sham potential in the TDKS system.<\/jats:p>","DOI":"10.1088\/2632-2153\/ace8f0","type":"journal-article","created":{"date-parts":[[2023,7,19]],"date-time":"2023-07-19T22:42:16Z","timestamp":1689806536000},"page":"035022","update-policy":"https:\/\/doi.org\/10.1088\/crossmark-policy","source":"Crossref","is-referenced-by-count":6,"title":["Machine-learning Kohn\u2013Sham potential from dynamics in time-dependent Kohn\u2013Sham systems"],"prefix":"10.1088","volume":"4","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5621-4962","authenticated-orcid":true,"given":"Jun","family":"Yang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"James","family":"Whitfield","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"266","published-online":{"date-parts":[[2023,8,21]]},"reference":[{"key":"mlstace8f0bib1","doi-asserted-by":"publisher","first-page":"997","DOI":"10.1103\/PhysRevLett.52.997","volume":"52","author":"Runge","year":"1984","journal-title":"Phys. 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