Enhancing materials property prediction by leveraging computational and experimental data using deep transfer learning

Machine-learning approaches based on DFT computations can greatly enhance materials discovery. Here the authors leverage existing large DFT-computational data sets and experimental observations by deep transfer learning to predict the formation energy of materials from their elemental compositions w...

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Autores principales:	Dipendra Jha, Kamal Choudhary, Francesca Tavazza, Wei-keng Liao, Alok Choudhary, Carelyn Campbell, Ankit Agrawal
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2019
Materias:	Science Q
Acceso en línea:	https://doaj.org/article/b5c082b4239448ccbbc16b581558d119
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spelling	oai:doaj.org-article:b5c082b4239448ccbbc16b581558d1192021-12-02T14:35:30ZEnhancing materials property prediction by leveraging computational and experimental data using deep transfer learning10.1038/s41467-019-13297-w2041-1723https://doaj.org/article/b5c082b4239448ccbbc16b581558d1192019-11-01T00:00:00Zhttps://doi.org/10.1038/s41467-019-13297-whttps://doaj.org/toc/2041-1723Machine-learning approaches based on DFT computations can greatly enhance materials discovery. Here the authors leverage existing large DFT-computational data sets and experimental observations by deep transfer learning to predict the formation energy of materials from their elemental compositions with high accuracy.Dipendra JhaKamal ChoudharyFrancesca TavazzaWei-keng LiaoAlok ChoudharyCarelyn CampbellAnkit AgrawalNature PortfolioarticleScienceQENNature Communications, Vol 10, Iss 1, Pp 1-12 (2019)
institution	DOAJ
collection	DOAJ
language	EN
topic	Science Q
spellingShingle	Science Q Dipendra Jha Kamal Choudhary Francesca Tavazza Wei-keng Liao Alok Choudhary Carelyn Campbell Ankit Agrawal Enhancing materials property prediction by leveraging computational and experimental data using deep transfer learning
description	Machine-learning approaches based on DFT computations can greatly enhance materials discovery. Here the authors leverage existing large DFT-computational data sets and experimental observations by deep transfer learning to predict the formation energy of materials from their elemental compositions with high accuracy.
format	article
author	Dipendra Jha Kamal Choudhary Francesca Tavazza Wei-keng Liao Alok Choudhary Carelyn Campbell Ankit Agrawal
author_facet	Dipendra Jha Kamal Choudhary Francesca Tavazza Wei-keng Liao Alok Choudhary Carelyn Campbell Ankit Agrawal
author_sort	Dipendra Jha
title	Enhancing materials property prediction by leveraging computational and experimental data using deep transfer learning
title_short	Enhancing materials property prediction by leveraging computational and experimental data using deep transfer learning
title_full	Enhancing materials property prediction by leveraging computational and experimental data using deep transfer learning
title_fullStr	Enhancing materials property prediction by leveraging computational and experimental data using deep transfer learning
title_full_unstemmed	Enhancing materials property prediction by leveraging computational and experimental data using deep transfer learning
title_sort	enhancing materials property prediction by leveraging computational and experimental data using deep transfer learning
publisher	Nature Portfolio
publishDate	2019
url	https://doaj.org/article/b5c082b4239448ccbbc16b581558d119
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Enhancing materials property prediction by leveraging computational and experimental data using deep transfer learning

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