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dc.contributorDepartment of Mechanical Engineeringen_US
dc.creatorLiu, Yen_US
dc.creatorEsan, OCen_US
dc.creatorPan, Zen_US
dc.creatorAn, Len_US
dc.publisherElsevier BVen_US
dc.rights© 2021 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (
dc.rightsThe following publication Liu, Y., Esan, O. C., Pan, Z., & An, L. (2021). Machine learning for advanced energy materials. Energy and AI, 3, 100049 is available at
dc.subjectEnergy materialsen_US
dc.subjectArtificial intelligenceen_US
dc.subjectMachine learningen_US
dc.subjectData-driven materials science and engineeringen_US
dc.subjectPrediction of materials propertiesen_US
dc.subjectDesign and discovery of energy materialsen_US
dc.titleMachine learning for advanced energy materialsen_US
dc.typeJournal/Magazine Articleen_US
dcterms.abstractThe screening of advanced materials coupled with the modeling of their quantitative structural-activity relationships has recently become one of the hot and trending topics in energy materials due to the diverse challenges, including low success probabilities, high time consumption, and high computational cost associated with the traditional methods of developing energy materials. Following this, new research concepts and technologies to promote the research and development of energy materials become necessary. The latest advancements in artificial intelligence and machine learning have therefore increased the expectation that data-driven materials science would revolutionize scientific discoveries towards providing new paradigms for the development of energy materials. Furthermore, the current advances in data-driven materials engineering also demonstrate that the application of machine learning technology would not only significantly facilitate the design and development of advanced energy materials but also enhance their discovery and deployment. In this article, the importance and necessity of developing new energy materials towards contributing to the global carbon neutrality are presented. A comprehensive introduction to the fundamentals of machine learning is also provided, including open-source databases, feature engineering, machine learning algorithms, and analysis of machine learning model. Afterwards, the latest progress in data-driven materials science and engineering, including alkaline ion battery materials, photovoltaic materials, catalytic materials, and carbon dioxide capture materials, is discussed. Finally, relevant clues to the successful applications of machine learning and the remaining challenges towards the development of advanced energy materials are highlighted.en_US
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationEnergy and AI, Mar. 2021, v. 3, 100049en_US
dcterms.isPartOfEnergy and AIen_US
dc.description.validate202105 bcvcen_US
dc.description.oaVersion of Recorden_US
dc.description.fundingTextRGC Ref. No. 15222018en_US
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