Machine learned features from density of states for accurate adsorption energy prediction

Materials databases generated by high-throughput computational screening, typically using density functional theory (DFT), have become valuable resources for discovering new heterogeneous catalysts, though the computational cost associated with generating them presents a crucial roadblock. Hence the...

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Detalles Bibliográficos
Autores principales: Fung, Victor, Hu, Guoxiang, Ganesh, P., Sumpter, Bobby G.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2021
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7782579/
https://www.ncbi.nlm.nih.gov/pubmed/33398014
http://dx.doi.org/10.1038/s41467-020-20342-6

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7782579/
https://www.ncbi.nlm.nih.gov/pubmed/33398014
http://dx.doi.org/10.1038/s41467-020-20342-6

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