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

Active learning—the field of machine learning (ML) dedicated to optimal experiment design—has played a part in science as far back as the 18th century when Laplace used it to guide his discovery of celestial mechanics. In this work, we focus a closed-loop, active learning-driven autonomous system on...

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Detalles Bibliográficos
Autores principales: Kusne, A. Gilad, Yu, Heshan, Wu, Changming, Zhang, Huairuo, Hattrick-Simpers, Jason, DeCost, Brian, Sarker, Suchismita, Oses, Corey, Toher, Cormac, Curtarolo, Stefano, Davydov, Albert V., Agarwal, Ritesh, Bendersky, Leonid A., Li, Mo, Mehta, Apurva, Takeuchi, Ichiro
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2020
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7686338/
https://www.ncbi.nlm.nih.gov/pubmed/33235197
http://dx.doi.org/10.1038/s41467-020-19597-w

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7686338/
https://www.ncbi.nlm.nih.gov/pubmed/33235197
http://dx.doi.org/10.1038/s41467-020-19597-w

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