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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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| Publicado no: | Sci Rep |
|---|---|
| Main Authors: | , , , , , , |
| Formato: | Artigo |
| Idioma: | Inglês |
| Publicado em: |
Nature Publishing Group UK
2020
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| Assuntos: | |
| Acesso em linha: | https://ncbi.nlm.nih.gov/pmc/articles/PMC7573639/ https://ncbi.nlm.nih.gov/pubmed/33077759 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1038/s41598-020-74394-1 |
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