{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,28]],"date-time":"2026-09-28T02:21:19Z","timestamp":1790562079683,"version":"4.1.0"},"reference-count":24,"publisher":"American Chemical Society (ACS)","issue":"7","license":[{"start":{"date-parts":[[2023,3,27]],"date-time":"2023-03-27T00:00:00Z","timestamp":1679875200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000161","name":"National Institute of Standards and Technology","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000161","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000190","name":"U.S. Department of Commerce","doi-asserted-by":"publisher","award":["70NANB19H005"],"award-info":[{"award-number":["70NANB19H005"]}],"id":[{"id":"10.13039\/100000190","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000015","name":"U.S. Department of Energy","doi-asserted-by":"publisher","award":["DE-SC0019358"],"award-info":[{"award-number":["DE-SC0019358"]}],"id":[{"id":"10.13039\/100000015","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000015","name":"U.S. Department of Energy","doi-asserted-by":"publisher","award":["DE-SC0021399"],"award-info":[{"award-number":["DE-SC0021399"]}],"id":[{"id":"10.13039\/100000015","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000147","name":"Division of Civil, Mechanical and Manufacturing Innovation","doi-asserted-by":"publisher","award":["CMMI-2053929"],"award-info":[{"award-number":["CMMI-2053929"]}],"id":[{"id":"10.13039\/100000147","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Northwestern Center for Nanocombinatorics"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,4,10]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>The applications of artificial intelligence, machine learning, and deep learning techniques in the field of materials science are becoming increasingly common due to their promising abilities to extract and utilize data-driven information from available data and accelerate materials discovery and design for future applications. In an attempt to assist with this process, we deploy predictive models for multiple material properties, given the composition of the material. The deep learning models described here are built using a cross-property deep transfer learning technique, which leverages source models trained on large data sets to build target models on small data sets with different properties. We deploy these models in an online software tool that takes a number of material compositions as input, performs preprocessing to generate composition-based attributes for each material, and feeds them into the predictive models to obtain up to 41 different material property values. The material property predictor is available online at http:\/\/ai.eecs.northwestern.edu\/MPpredictor.<\/jats:p>","DOI":"10.1021\/acs.jcim.3c00307","type":"journal-article","created":{"date-parts":[[2023,3,27]],"date-time":"2023-03-27T18:33:17Z","timestamp":1679941997000},"page":"1865-1871","source":"Crossref","is-referenced-by-count":36,"title":["MPpredictor: An\nArtificial Intelligence-Driven Web\nTool for Composition-Based Material Property Prediction"],"prefix":"10.1021","volume":"63","author":[{"given":"Vishu","family":"Gupta","sequence":"first","affiliation":[{"name":"Northwestern University , , , ,","place":["Evanston, Illinois, United States, 60208"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9737-8074","authenticated-orcid":true,"given":"Kamal","family":"Choudhary","sequence":"additional","affiliation":[{"name":"National Institute of Standards and Technology , , , ,","place":["Gaithersburg, Maryland, United States, 20899"]},{"name":"Theiss Research , , ,","place":["La Jolla, California, United States, 92037"]},{"name":"DeepMaterials LLC , , ,","place":["Silver Spring, Maryland, United\nStates, 20906"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuwei","family":"Mao","sequence":"additional","affiliation":[{"name":"Northwestern University , , , ,","place":["Evanston, Illinois, United States, 60208"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kewei","family":"Wang","sequence":"additional","affiliation":[{"name":"Northwestern University , , , ,","place":["Evanston, Illinois, United States, 60208"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Francesca","family":"Tavazza","sequence":"additional","affiliation":[{"name":"National Institute of Standards and Technology , , , ,","place":["Gaithersburg, Maryland, United States, 20899"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Carelyn","family":"Campbell","sequence":"additional","affiliation":[{"name":"National Institute of Standards and Technology , , , ,","place":["Gaithersburg, Maryland, United States, 20899"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wei-keng","family":"Liao","sequence":"additional","affiliation":[{"name":"Northwestern University , , , ,","place":["Evanston, Illinois, United States, 60208"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alok","family":"Choudhary","sequence":"additional","affiliation":[{"name":"Northwestern University , , , ,","place":["Evanston, Illinois, United States, 60208"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5519-0302","authenticated-orcid":true,"given":"Ankit","family":"Agrawal","sequence":"additional","affiliation":[{"name":"Northwestern University , , , ,","place":["Evanston, Illinois, United States, 60208"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"316","published-online":{"date-parts":[[2023,3,27]]},"reference":[{"key":"2026081804435157100_cit1","doi-asserted-by":"publisher","first-page":"053208","DOI":"10.1063\/1.4946894","article-title":"Perspective:\nMaterials informatics and big data: Realization\nof the \u201cfourth paradigm\u201d of science in materials science","volume":"4","author":"Agrawal","year":"2016","journal-title":"Apl Materials"},{"key":"2026081804435157100_cit2","doi-asserted-by":"publisher","first-page":"171","DOI":"10.1146\/annurev-matsci-070214-020844","article-title":"Materials\ndata science: current status and future outlook","volume":"45","author":"Kalidindi","year":"2015","journal-title":"Annu. 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Res."},{"key":"2026081804435157100_cit4","doi-asserted-by":"publisher","first-page":"779","DOI":"10.1557\/mrc.2019.73","article-title":"Deep materials\ninformatics: Applications of deep learning\nin materials science","volume":"9","author":"Agrawal","year":"2019","journal-title":"MRS Commun."},{"key":"2026081804435157100_cit5","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41524-022-00734-6","article-title":"Recent advances and applications\nof deep learning methods\nin materials science","volume":"8","author":"Choudhary","year":"2022","journal-title":"npj Computational Materials"},{"key":"2026081804435157100_cit6","doi-asserted-by":"publisher","first-page":"566","DOI":"10.3390\/70800566","article-title":"Chemoinformatics and\ndrug discovery","volume":"7","author":"Xu","year":"2002","journal-title":"Molecules"},{"key":"2026081804435157100_cit7","doi-asserted-by":"publisher","first-page":"283","DOI":"10.1007\/s11030-006-9041-5","article-title":"Cheminformatics\nanalysis and learning in a data pipelining environment","volume":"10","author":"Hassan","year":"2006","journal-title":"Molecular diversity"},{"key":"2026081804435157100_cit8","doi-asserted-by":"publisher","first-page":"1538","DOI":"10.1016\/j.drudis.2018.05.010","article-title":"Machine\nlearning\nin chemoinformatics and drug discovery","volume":"23","author":"Lo","year":"2018","journal-title":"Drug\ndiscovery today"},{"key":"2026081804435157100_cit9","volume-title":"The Fourth Paradigm: Data-Intensive\nScientific Discovery","author":"Hey","year":"2009"},{"key":"2026081804435157100_cit10","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3846\/16484142.2013.777941","article-title":"Knowledge\ndiscovery and data mining in pavement inverse analysis","volume":"28","author":"Gopalakrishnan","year":"2013","journal-title":"Transport"},{"key":"2026081804435157100_cit11","doi-asserted-by":"publisher","first-page":"90","DOI":"10.1186\/2193-9772-3-8","article-title":"Exploration of data science techniques\nto predict fatigue strength of steel from composition and processing\nparameters","volume":"3","author":"Agrawal","year":"2014","journal-title":"Integrating Materials and Manufacturing\nInnovation"},{"key":"2026081804435157100_cit12","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/srep11551","article-title":"A predictive machine learning approach for microstructure\noptimization and materials design","volume":"5","author":"Liu","year":"2015","journal-title":"Sci. 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Rep."},{"key":"2026081804435157100_cit25","doi-asserted-by":"publisher","first-page":"343","DOI":"10.1137\/1.9781611977172.39","article-title":"Brnet: Branched residual\nnetwork for fast and accurate predictive modeling of materials properties.\nIn Proceedings of the 2022 SIAM international conference on data mining\n(SDM)","author":"Gupta","year":"2022","journal-title":"Society for Industrial and Applied Mathematics"},{"key":"2026081804435157100_cit18","unstructured":"Materials Genome Initiative for Global Competitiveness; National Science and Technology Council, 2011."},{"key":"2026081804435157100_cit19","doi-asserted-by":"publisher","first-page":"334","DOI":"10.1007\/s11837-014-0894-2","article-title":"Materials\ngenome initiative: advances and initiatives","volume":"66","author":"Drosback","year":"2014","journal-title":"JOM"},{"key":"2026081804435157100_cit20","unstructured":"Initiative, M. G.\n          \n          Strategic Plan, 2021. 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E.; Meredig, B.\n          Reproducibility in high-throughput\ndensity functional\ntheory: a comparison of AFLOW, Materials Project, and OQMD. arXiv Preprint, arXiv:2007.01988, 2020."}],"container-title":["Journal of Chemical Information\nand Modeling"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/pubs.acs.org\/doi\/pdf\/10.1021\/acs.jcim.3c00307","content-type":"application\/pdf","content-version":"vor","intended-application":"unspecified"},{"URL":"https:\/\/pubs.acs.org\/jcisd8\/article-pdf\/63\/7\/1865\/8910648\/ci3c00307.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/pubs.acs.org\/jcisd8\/article-pdf\/63\/7\/1865\/8910648\/ci3c00307.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T09:28:13Z","timestamp":1787045293000},"score":1,"resource":{"primary":{"URL":"https:\/\/pubs.acs.org\/jcisd8\/article\/63\/7\/1865\/850411\/MPpredictor-An-Artificial-Intelligence-Driven-Web"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,3,27]]},"references-count":24,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2023,3,27]]},"published-print":{"date-parts":[[2023,4,10]]}},"URL":"https:\/\/doi.org\/10.1021\/acs.jcim.3c00307","relation":{},"ISSN":["1549-9596","1549-960X"],"issn-type":[{"value":"1549-9596","type":"print"},{"value":"1549-960X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,3,27]]}}}