Force field optimization by end-to-end differentiable atomistic simulation Article Swipe
Abhijeet Gangan
,
Samuel S. Schoenholz
,
Ekin D. Cubuk
,
Mathieu Bauchy
,
N. M. Anoop Krishnan
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2409.13844
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2409.13844
The accuracy of atomistic simulations depends on the precision of force fields. Traditional numerical methods often struggle to optimize the empirical force field parameters for reproducing target properties. Recent approaches rely on training these force fields based on forces and energies from first-principle simulations. However, it is unclear whether these approaches will enable capturing complex material responses such as vibrational, or elastic properties. To this extent, we introduce a framework, employing inner loop simulations and outer loop optimization, that exploits automatic differentiation for both property prediction and force-field optimization by computing gradients of the simulation analytically. We demonstrate the approach by optimizing classical Stillinger-Weber and EDIP potentials for silicon systems to reproduce the elastic constants, vibrational density of states, and phonon dispersion. We also demonstrate how a machine-learned potential can be fine-tuned using automatic differentiation to reproduce any target property such as radial distribution functions. Interestingly, the resulting force field exhibits improved accuracy and generalizability to unseen temperatures than those fine-tuned on energies and forces. Finally, we demonstrate the extension of the approach to optimize the force fields towards multiple target properties. Altogether, differentiable simulations, through the analytical computation of their gradients, offer a powerful tool for both theoretical exploration and practical applications toward understanding physical systems and materials.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2409.13844
- https://arxiv.org/pdf/2409.13844
- OA Status
- green
- Cited By
- 1
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4403752747
All OpenAlex metadata
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https://openalex.org/W4403752747Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2409.13844Digital Object Identifier
- Title
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Force field optimization by end-to-end differentiable atomistic simulationWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-09-20Full publication date if available
- Authors
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Abhijeet Gangan, Samuel S. Schoenholz, Ekin D. Cubuk, Mathieu Bauchy, N. M. Anoop KrishnanList of authors in order
- Landing page
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https://arxiv.org/abs/2409.13844Publisher landing page
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https://arxiv.org/pdf/2409.13844Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
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greenOpen access status per OpenAlex
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https://arxiv.org/pdf/2409.13844Direct OA link when available
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Force field (fiction), Differentiable function, End-to-end principle, Field (mathematics), Molecular dynamics, Computer science, Physics, Mathematics, Mathematical analysis, Pure mathematics, Artificial intelligence, Quantum mechanicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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1Total citation count in OpenAlex
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2025: 1Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.properties. | 27, 62, 181 |
| abstract_inverted_index.reproducing | 25 |
| abstract_inverted_index.simulations | 4, 73 |
| abstract_inverted_index.theoretical | 198 |
| abstract_inverted_index.vibrational | 115 |
| abstract_inverted_index.applications | 202 |
| abstract_inverted_index.distribution | 143 |
| abstract_inverted_index.optimization | 88 |
| abstract_inverted_index.simulations, | 184 |
| abstract_inverted_index.simulations. | 43 |
| abstract_inverted_index.temperatures | 157 |
| abstract_inverted_index.vibrational, | 59 |
| abstract_inverted_index.analytically. | 95 |
| abstract_inverted_index.optimization, | 77 |
| abstract_inverted_index.understanding | 204 |
| abstract_inverted_index.Interestingly, | 145 |
| abstract_inverted_index.differentiable | 183 |
| abstract_inverted_index.differentiation | 81, 134 |
| abstract_inverted_index.first-principle | 42 |
| abstract_inverted_index.machine-learned | 127 |
| abstract_inverted_index.Stillinger-Weber | 103 |
| abstract_inverted_index.generalizability | 154 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 5 |
| citation_normalized_percentile |