Many-body machine learning models for water, acetonitrile, and methanol
GDML, GAP, and SchNet models trained on 1-, 2-, and 3-body energies and forces of water, acetonitrile, and methanol. Energies and forces were computed at the MP2/def2-TZVP level of theory in ORCA v4.2.0. Data sets, training scripts, and analyses of these potentials are available here. Applications o...
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| Format: | Dataset |
| Language: | unknown |
| Published: |
2022
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| Online Access: | https://zenodo.org/record/7112164 https://doi.org/10.5281/zenodo.7112164 |
| Summary: | GDML, GAP, and SchNet models trained on 1-, 2-, and 3-body energies and forces of water, acetonitrile, and methanol. Energies and forces were computed at the MP2/def2-TZVP level of theory in ORCA v4.2.0. Data sets, training scripts, and analyses of these potentials are available here. Applications of these models on molecular dynamics simulations are found here. |
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