{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,24]],"date-time":"2026-08-24T11:21:52Z","timestamp":1787570512165,"version":"build-2736575974"},"reference-count":54,"publisher":"AIP Publishing","issue":"8","funder":[{"name":"The Fund for Bilateral Relationships between China and Hungary in Science and Technology","award":["2021-1.2.4-T\u00c9T-2021-00055"],"award-info":[{"award-number":["2021-1.2.4-T\u00c9T-2021-00055"]}]},{"name":"COST Action NanoSpace","award":["CA21126"],"award-info":[{"award-number":["CA21126"]}]},{"name":"COST Action PLANETS","award":["CA22133"],"award-info":[{"award-number":["CA22133"]}]}],"content-domain":{"domain":["pubs.aip.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2026,8,28]]},"abstract":"<jats:p>We present a machine learning (ML) approach for the accelerated extraction of optical properties from the experimental reflection electron energy loss spectroscopy (REELS) spectra by the reverse Monte Carlo (RMC) method. Taking platinum (Pt) as an example, experimental REELS spectra recorded at incident electron energies of 2.0 and 1.5\u00a0keV over an energy loss range of 0\u2013200\u00a0eV are processed by one-dimensional convolutional neural networks trained specifically for each incident energy. The training processes incorporate the physical constraints based on the perfect-screening (ps-) and oscillator-strength (f-) sum rules of ELF into the loss functions to guarantee the validity of the results. The networks are trained on datasets generated by the RMC method, which pairs the simulated REELS spectra with the corresponding energy loss functions (ELFs). This approach enables the direct prediction of the ELF and optical constants from measured REELS spectra with reduced computational cost as compared to the previous RMC method. The predicted ELFs from ML exhibit high accuracy as verified by sum rules, and the optical properties of Pt are derived from these ELFs.<\/jats:p>","DOI":"10.1063\/5.0344660","type":"journal-article","created":{"date-parts":[[2026,8,24]],"date-time":"2026-08-24T10:35:31Z","timestamp":1787567731000},"update-policy":"https:\/\/doi.org\/10.1063\/aip-crossmark-policy-page","source":"Crossref","is-referenced-by-count":0,"title":["Machine learning enhanced prediction of optical properties via reverse Monte Carlo method"],"prefix":"10.1063","volume":"165","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-7543-4468","authenticated-orcid":false,"given":"Z.","family":"Li","sequence":"first","affiliation":[{"name":"Department of Physics, University of Science and Technology of China 1 , Hefei 230026, Anhui,","place":["People\u2019s Republic of China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-4394-8822","authenticated-orcid":false,"given":"J. 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Box 49, H-1525 Budapest,","place":["Hungary"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"B.","family":"Da","sequence":"additional","affiliation":[{"name":"Department of Precision Machinery and Precision Instrumentation, School of Engineering, University of Science and Technology of China 4 , Hefei 230026, Anhui,","place":["People\u2019s Republic of China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8772-8472","authenticated-orcid":false,"given":"K.","family":"T\u0151k\u00e9si","sequence":"additional","affiliation":[{"name":"HUN-REN Institute for Nuclear Research 5 , P.O. Box 51, Debrecen,","place":["Hungary"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5767-1145","authenticated-orcid":false,"given":"Z. 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