Harnessing interpretable and unsupervised machine learning to address big data from modern X-ray diffraction

The information content of crystalline materials becomes astronomical when collective electronic behavior and their fluctuations are taken into account. In the past decade, improvements in source brightness and detector technology at modern X-ray facilities have allowed a dramatically increased frac...

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Detalles Bibliográficos
Autores principales: Venderley, Jordan, Mallayya, Krishnanand, Matty, Michael, Krogstad, Matthew, Ruff, Jacob, Pleiss, Geoff, Kishore, Varsha, Mandrus, David, Phelan, Daniel, Poudel, Lekhanath, Wilson, Andrew Gordon, Weinberger, Kilian, Upreti, Puspa, Norman, Michael, Rosenkranz, Stephan, Osborn, Raymond, Kim, Eun-Ah
Formato: Online Artículo Texto
Lenguaje:English
Publicado: National Academy of Sciences 2022
Materias:
Physical Sciences
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9214512/
https://www.ncbi.nlm.nih.gov/pubmed/35679347
http://dx.doi.org/10.1073/pnas.2109665119

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9214512/
https://www.ncbi.nlm.nih.gov/pubmed/35679347
http://dx.doi.org/10.1073/pnas.2109665119

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