On-the-fly closed-loop materials discovery via Bayesian active learning

Machine learning driven research holds big promise towards accelerating materials’ discovery. Here the authors demonstrate CAMEO, which integrates active learning Bayesian optimization with practical experiments execution, for the discovery of new phase- change materials using X-ray diffraction expe...

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Autores principales:	A. Gilad Kusne, Heshan Yu, Changming Wu, Huairuo Zhang, Jason Hattrick-Simpers, Brian DeCost, Suchismita Sarker, Corey Oses, Cormac Toher, Stefano Curtarolo, Albert V. Davydov, Ritesh Agarwal, Leonid A. Bendersky, Mo Li, Apurva Mehta, Ichiro Takeuchi
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2020
Materias:	Science Q
Acceso en línea:	https://doaj.org/article/04164405f2b441b89a1dbae389eb4d1c
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spelling	oai:doaj.org-article:04164405f2b441b89a1dbae389eb4d1c2021-12-02T14:16:55ZOn-the-fly closed-loop materials discovery via Bayesian active learning10.1038/s41467-020-19597-w2041-1723https://doaj.org/article/04164405f2b441b89a1dbae389eb4d1c2020-11-01T00:00:00Zhttps://doi.org/10.1038/s41467-020-19597-whttps://doaj.org/toc/2041-1723Machine learning driven research holds big promise towards accelerating materials’ discovery. Here the authors demonstrate CAMEO, which integrates active learning Bayesian optimization with practical experiments execution, for the discovery of new phase- change materials using X-ray diffraction experiments.A. Gilad KusneHeshan YuChangming WuHuairuo ZhangJason Hattrick-SimpersBrian DeCostSuchismita SarkerCorey OsesCormac ToherStefano CurtaroloAlbert V. DavydovRitesh AgarwalLeonid A. BenderskyMo LiApurva MehtaIchiro TakeuchiNature PortfolioarticleScienceQENNature Communications, Vol 11, Iss 1, Pp 1-11 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Science Q
spellingShingle	Science Q A. Gilad Kusne Heshan Yu Changming Wu Huairuo Zhang Jason Hattrick-Simpers Brian DeCost Suchismita Sarker Corey Oses Cormac Toher Stefano Curtarolo Albert V. Davydov Ritesh Agarwal Leonid A. Bendersky Mo Li Apurva Mehta Ichiro Takeuchi On-the-fly closed-loop materials discovery via Bayesian active learning
description	Machine learning driven research holds big promise towards accelerating materials’ discovery. Here the authors demonstrate CAMEO, which integrates active learning Bayesian optimization with practical experiments execution, for the discovery of new phase- change materials using X-ray diffraction experiments.
format	article
author	A. Gilad Kusne Heshan Yu Changming Wu Huairuo Zhang Jason Hattrick-Simpers Brian DeCost Suchismita Sarker Corey Oses Cormac Toher Stefano Curtarolo Albert V. Davydov Ritesh Agarwal Leonid A. Bendersky Mo Li Apurva Mehta Ichiro Takeuchi
author_facet	A. Gilad Kusne Heshan Yu Changming Wu Huairuo Zhang Jason Hattrick-Simpers Brian DeCost Suchismita Sarker Corey Oses Cormac Toher Stefano Curtarolo Albert V. Davydov Ritesh Agarwal Leonid A. Bendersky Mo Li Apurva Mehta Ichiro Takeuchi
author_sort	A. Gilad Kusne
title	On-the-fly closed-loop materials discovery via Bayesian active learning
title_short	On-the-fly closed-loop materials discovery via Bayesian active learning
title_full	On-the-fly closed-loop materials discovery via Bayesian active learning
title_fullStr	On-the-fly closed-loop materials discovery via Bayesian active learning
title_full_unstemmed	On-the-fly closed-loop materials discovery via Bayesian active learning
title_sort	on-the-fly closed-loop materials discovery via bayesian active learning
publisher	Nature Portfolio
publishDate	2020
url	https://doaj.org/article/04164405f2b441b89a1dbae389eb4d1c
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On-the-fly closed-loop materials discovery via Bayesian active learning

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