Nutmeg and SPICE: Models and Data for Biomolecular Machine Learning
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arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866913499908669440 |
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| author | Eastman, Peter Pritchard, Benjamin P. Chodera, John D. Markland, Thomas E. |
| author_facet | Eastman, Peter Pritchard, Benjamin P. Chodera, John D. Markland, Thomas E. |
| contents | We describe version 2 of the SPICE dataset, a collection of quantum chemistry calculations for training machine learning potentials. It expands on the original dataset by adding much more sampling of chemical space and more data on non-covalent interactions. We train a set of potential energy functions called Nutmeg on it. They are based on the TensorNet architecture. They use a novel mechanism to improve performance on charged and polar molecules, injecting precomputed partial charges into the model to provide a reference for the large scale charge distribution. Evaluation of the new models shows they do an excellent job of reproducing energy differences between conformations, even on highly charged molecules or ones that are significantly larger than the molecules in the training set. They also produce stable molecular dynamics trajectories, and are fast enough to be useful for routine simulation of small molecules. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_13112 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Nutmeg and SPICE: Models and Data for Biomolecular Machine Learning Eastman, Peter Pritchard, Benjamin P. Chodera, John D. Markland, Thomas E. Chemical Physics Machine Learning We describe version 2 of the SPICE dataset, a collection of quantum chemistry calculations for training machine learning potentials. It expands on the original dataset by adding much more sampling of chemical space and more data on non-covalent interactions. We train a set of potential energy functions called Nutmeg on it. They are based on the TensorNet architecture. They use a novel mechanism to improve performance on charged and polar molecules, injecting precomputed partial charges into the model to provide a reference for the large scale charge distribution. Evaluation of the new models shows they do an excellent job of reproducing energy differences between conformations, even on highly charged molecules or ones that are significantly larger than the molecules in the training set. They also produce stable molecular dynamics trajectories, and are fast enough to be useful for routine simulation of small molecules. |
| title | Nutmeg and SPICE: Models and Data for Biomolecular Machine Learning |
| topic | Chemical Physics Machine Learning |
| url | https://arxiv.org/abs/2406.13112 |