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Machine learning for the prediction of molecular dipole moments obtained by density functional theory
Machine learning (ML) algorithms were explored for the fast estimation of molecular dipole moments calculated by density functional theory (DFT) by B3LYP/6-31G(d,p) on the basis of molecular descriptors generated from DFT-optimized geometries and partial atomic charges obtained by empirical or ML sc...
| Autores principales: | , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
Springer International Publishing
2018
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| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6104469/ https://www.ncbi.nlm.nih.gov/pubmed/30136001 http://dx.doi.org/10.1186/s13321-018-0296-5 |