A machine learning correction for DFT non-covalent interactions based on the S22, S66 and X40 benchmark databases

BACKGROUND: Non-covalent interactions (NCIs) play critical roles in supramolecular chemistries; however, they are difficult to measure. Currently, reliable computational methods are being pursued to meet this challenge, but the accuracy of calculations based on low levels of theory is not satisfacto...

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Detalles Bibliográficos
Autores principales: Gao, Ting, Li, Hongzhi, Li, Wenze, Li, Lin, Fang, Chao, Li, Hui, Hu, LiHong, Lu, Yinghua, Su, Zhong-Min
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer International Publishing 2016
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4855356/
https://www.ncbi.nlm.nih.gov/pubmed/27148408
http://dx.doi.org/10.1186/s13321-016-0133-7

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4855356/
https://www.ncbi.nlm.nih.gov/pubmed/27148408
http://dx.doi.org/10.1186/s13321-016-0133-7

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