Autonomous materials synthesis via hierarchical active learning of nonequilibrium phase diagrams

Autonomous experimentation enabled by artificial intelligence offers a new paradigm for accelerating scientific discovery. Nonequilibrium materials synthesis is emblematic of complex, resource-intensive experimentation whose acceleration would be a watershed for materials discovery. We demonstrate a...

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Detalles Bibliográficos
Autores principales: Ament, Sebastian, Amsler, Maximilian, Sutherland, Duncan R., Chang, Ming-Chiang, Guevarra, Dan, Connolly, Aine B., Gregoire, John M., Thompson, Michael O., Gomes, Carla P., van Dover, R. Bruce
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Association for the Advancement of Science 2021
Materias:
Physical and Materials Sciences
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8682983/
https://www.ncbi.nlm.nih.gov/pubmed/34919429
http://dx.doi.org/10.1126/sciadv.abg4930

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8682983/
https://www.ncbi.nlm.nih.gov/pubmed/34919429
http://dx.doi.org/10.1126/sciadv.abg4930

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