Deep neural networks for accurate predictions of crystal stability

Predicting the stability of crystals is one of the central problems in materials science. Today, density functional theory (DFT) calculations remain comparatively expensive and scale poorly with system size. Here we show that deep neural networks utilizing just two descriptors—the Pauling electroneg...

Descripción completa

Detalles Bibliográficos
Autores principales: Ye, Weike, Chen, Chi, Wang, Zhenbin, Chu, Iek-Heng, Ong, Shyue Ping
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2018
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6143552/
https://www.ncbi.nlm.nih.gov/pubmed/30228262
http://dx.doi.org/10.1038/s41467-018-06322-x

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6143552/
https://www.ncbi.nlm.nih.gov/pubmed/30228262
http://dx.doi.org/10.1038/s41467-018-06322-x

Ejemplares similares