Machine learning to tame divergent density functional approximations: a new path to consensus materials design principles

Virtual high-throughput screening (VHTS) with density functional theory (DFT) and machine-learning (ML)-acceleration is essential in rapid materials discovery. By necessity, efficient DFT-based workflows are carried out with a single density functional approximation (DFA). Nevertheless, properties e...

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Detalles Bibliográficos
Autores principales: Duan, Chenru, Chen, Shuxin, Taylor, Michael G., Liu, Fang, Kulik, Heather J.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: The Royal Society of Chemistry 2021
Materias:
Chemistry
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8513898/
https://www.ncbi.nlm.nih.gov/pubmed/34745533
http://dx.doi.org/10.1039/d1sc03701c

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8513898/
https://www.ncbi.nlm.nih.gov/pubmed/34745533
http://dx.doi.org/10.1039/d1sc03701c

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