{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,29]],"date-time":"2026-10-29T03:08:14Z","timestamp":1793243294172,"version":"4.5.0"},"reference-count":28,"publisher":"IOP Publishing","issue":"3","license":[{"start":{"date-parts":[[2021,4,22]],"date-time":"2021-04-22T00:00:00Z","timestamp":1619049600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2021,4,22]],"date-time":"2021-04-22T00:00:00Z","timestamp":1619049600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/iopscience.iop.org\/info\/page\/text-and-data-mining"}],"content-domain":{"domain":["iopscience.iop.org"],"crossmark-restriction":false},"short-container-title":["Mach. Learn.: Sci. Technol."],"published-print":{"date-parts":[[2021,9,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Neutron reflectometry (NR) is a powerful tool for probing thin films at length scales down to nanometers. We investigated the use of a neural network to predict a two-layer thin film structure to model a given measured reflectivity curve. Application of this neural network to predict a thin film structure revealed that it was accurate and could provide an excellent starting point for traditional fitting methods. Employing prediction-guided fitting has considerable potential for more rapidly producing a result compared to the labor-intensive but commonly-used approach of trial and error searches prior to refinement. A deeper look at the stability of the predictive power of the neural network against statistical fluctuations of measured reflectivity profiles showed that the predictions are stable. We conclude that the approach presented here can provide valuable assistance to users of NR and should be further extended for use in studies of more complex n-layer thin film systems. This result also opens up the possibility of developing adaptive measurement systems in the future.<\/jats:p>","DOI":"10.1088\/2632-2153\/abf257","type":"journal-article","created":{"date-parts":[[2021,3,26]],"date-time":"2021-03-26T22:15:30Z","timestamp":1616796930000},"page":"035001","update-policy":"https:\/\/doi.org\/10.1088\/crossmark-policy","source":"Crossref","is-referenced-by-count":22,"title":["Machine learning for neutron reflectometry data analysis of two-layer thin films\n                  <sup>*<\/sup>"],"prefix":"10.1088","volume":"2","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5560-6478","authenticated-orcid":false,"given":"Mathieu","family":"Doucet","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Richard K","family":"Archibald","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"William T","family":"Heller","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"266","published-online":{"date-parts":[[2021,4,22]]},"reference":[{"key":"mlstabf257bib1","article-title":"Thermal-neutron scattering lengths and cross sections for condensed-matter research","volume":"AECL-8490","author":"Sears","year":"1984"},{"key":"mlstabf257bib2","author":"Sivia","year":"2011"},{"key":"mlstabf257bib3","doi-asserted-by":"publisher","DOI":"10.2172\/1630823","article-title":"BES Roundtable on producing and managing large scientific data with artificial intelligence and machine learning","author":"Ratner","year":"2019"},{"key":"mlstabf257bib4","doi-asserted-by":"publisher","first-page":"11","DOI":"10.1557\/mrc.2019.166","article-title":"Deep learning-based super-resolution for small-angle neutron scattering data: attempt to accelerate experimental workflow","volume":"10","author":"Chang","year":"2020","journal-title":"MRS Commun."},{"key":"mlstabf257bib5","doi-asserted-by":"publisher","first-page":"1342","DOI":"10.1107\/S1600576719013311","article-title":"Fast fitting of reflectivity data of growing thin films using neural networks","volume":"52","author":"Greco","year":"2019","journal-title":"J. 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