Reversible molecular simulation for training classical and machine learning force fields
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arXiv
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866916690166546432 |
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| author | Greener, Joe G |
| author_facet | Greener, Joe G |
| contents | The next generation of force fields for molecular dynamics will be developed using a wealth of data. Training systematically with experimental data remains a challenge, however, especially for machine learning potentials. Differentiable molecular simulation calculates gradients of observables with respect to parameters through molecular dynamics trajectories. Here we improve this approach by explicitly calculating gradients using a reverse-time simulation with effectively constant memory cost and a computation count similar to the forward simulation. The method is applied to learn all-atom water and gas diffusion models with different functional forms, and to train a machine learning potential for diamond from scratch. Comparison to ensemble reweighting indicates that reversible simulation can provide more accurate gradients and train to match time-dependent observables. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_04374 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Reversible molecular simulation for training classical and machine learning force fields Greener, Joe G Biomolecules The next generation of force fields for molecular dynamics will be developed using a wealth of data. Training systematically with experimental data remains a challenge, however, especially for machine learning potentials. Differentiable molecular simulation calculates gradients of observables with respect to parameters through molecular dynamics trajectories. Here we improve this approach by explicitly calculating gradients using a reverse-time simulation with effectively constant memory cost and a computation count similar to the forward simulation. The method is applied to learn all-atom water and gas diffusion models with different functional forms, and to train a machine learning potential for diamond from scratch. Comparison to ensemble reweighting indicates that reversible simulation can provide more accurate gradients and train to match time-dependent observables. |
| title | Reversible molecular simulation for training classical and machine learning force fields |
| topic | Biomolecules |
| url | https://arxiv.org/abs/2412.04374 |