Machine Learning Based Approaches to Accelerate Energy Materials Discovery and Optimization

ADVERTISEMENT RETURN TO ISSUEPREVEnergy FocusNEXTMachine Learning Based Approaches to Accelerate Energy Materials Discovery and OptimizationDilip KrishnamurthyDilip KrishnamurthyDepartment of Mechanical Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, United StatesWilton E. Scott Institute for Energy Innovation, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, United StatesMore by Dilip Krishnamurthyhttp://orcid.org/0000-0001-8231-5492, Hasso WeilandHasso WeilandScandium International Mining Corporation, Sparks, Nevada 89431, United StatesMore by Hasso Weiland, Amir Barati FarimaniAmir Barati FarimaniDepartment of Mechanical Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, United StatesMore by Amir Barati Farimani, Erin AntonoErin AntonoCitrine Informatics, Redwood City, California 94063, United StatesMore by Erin Antono, Josh GreenJosh GreenCitrine Informatics, Pittsburgh, Pennsylvania 15206, United StatesMore by Josh Green, and Venkatasubramanian Viswanathan*Venkatasubramanian ViswanathanDepartment of Mechanical Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, United StatesWilton E. Scott Institute for Energy Innovation, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, United States*E-mail: [email protected]More by Venkatasubramanian Viswanathanhttp://orcid.org/0000-0003-1060-5495Cite this: ACS Energy Lett. 2019, 4, 1, 187–191Publication Date (Web):December 7, 2018Publication History Received21 November 2018Accepted27 November 2018Published online7 December 2018Published inissue 11 January 2019https://pubs.acs.org/doi/10.1021/acsenergylett.8b02278https://doi.org/10.1021/acsenergylett.8b02278article-commentaryACS PublicationsCopyright © 2018 American Chemical Society. This publication is available under these Terms of Use. Request reuse permissions This publication is free to access through this site. Learn MoreArticle Views5568Altmetric-Citations24LEARN ABOUT THESE METRICSArticle Views are the COUNTER-compliant sum of full text article downloads since November 2008 (both PDF and HTML) across all institutions and individuals. These metrics are regularly updated to reflect usage leading up to the last few days.Citations are the number of other articles citing this article, calculated by Crossref and updated daily. Find more information about Crossref citation counts.The Altmetric Attention Score is a quantitative measure of the attention that a research article has received online. Clicking on the donut icon will load a page at altmetric.com with additional details about the score and the social media presence for the given article. Find more information on the Altmetric Attention Score and how the score is calculated. Share Add toView InAdd Full Text with ReferenceAdd Description ExportRISCitationCitation and abstractCitation and referencesMore Options Share onFacebookTwitterWechatLinked InRedditEmail PDF (4 MB) Get e-AlertscloseSUBJECTS:Curriculum,Energy,Machine learning,Materials,Optimization Get e-Alerts