Autonomous materials discovery driven by Gaussian process regression with inhomogeneous measurement noise and anisotropic kernels

A majority of experimental disciplines face the challenge of exploring large and high-dimensional parameter spaces in search of new scientific discoveries. Materials science is no exception; the wide variety of synthesis, processing, and environmental conditions that influence material properties gi...

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Detalles Bibliográficos
Autores principales: Noack, Marcus M., Doerk, Gregory S., Li, Ruipeng, Streit, Jason K., Vaia, Richard A., Yager, Kevin G., Fukuto, Masafumi
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/PMC7573639/
https://www.ncbi.nlm.nih.gov/pubmed/33077759
http://dx.doi.org/10.1038/s41598-020-74394-1

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7573639/
https://www.ncbi.nlm.nih.gov/pubmed/33077759
http://dx.doi.org/10.1038/s41598-020-74394-1

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