Machine learning for the prediction of molecular dipole moments obtained by density functional theory
Abstract 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...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
BMC
2018-08-01
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| Series: | Journal of Cheminformatics |
| Subjects: | |
| Online Access: | http://link.springer.com/article/10.1186/s13321-018-0296-5 |