Quantum chemical accuracy from density functional approximations via machine learning
High-level ab initio quantum chemical methods carry a high computational burden, thus limiting their applicability. Here, the authors employ machine learning to generate coupled-cluster energies and forces at chemical accuracy for geometry optimization and molecular dynamics from DFT densities.
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2020-10-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-020-19093-1 |
| Summary: | High-level ab initio quantum chemical methods carry a high computational burden, thus limiting their applicability. Here, the authors employ machine learning to generate coupled-cluster energies and forces at chemical accuracy for geometry optimization and molecular dynamics from DFT densities. |
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| ISSN: | 2041-1723 |