FEAT: Free energy Estimators with Adaptive Transport
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arXiv
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| Autori principali: | , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866915734909616128 |
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| author | He, Jiajun Du, Yuanqi Vargas, Francisco Wang, Yuanqing Gomes, Carla P. Hernández-Lobato, José Miguel Vanden-Eijnden, Eric |
| author_facet | He, Jiajun Du, Yuanqi Vargas, Francisco Wang, Yuanqing Gomes, Carla P. Hernández-Lobato, José Miguel Vanden-Eijnden, Eric |
| contents | We present Free energy Estimators with Adaptive Transport (FEAT), a novel framework for free energy estimation -- a critical challenge across scientific domains. FEAT leverages learned transports implemented via stochastic interpolants and provides consistent, minimum-variance estimators based on escorted Jarzynski equality and controlled Crooks theorem, alongside variational upper and lower bounds on free energy differences. Unifying equilibrium and non-equilibrium methods under a single theoretical framework, FEAT establishes a principled foundation for neural free energy calculations. Experimental validation on toy examples, molecular simulations, and quantum field theory demonstrates improvements over existing learning-based methods. Our PyTorch implementation is available at https://github.com/jiajunhe98/FEAT. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_11516 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | FEAT: Free energy Estimators with Adaptive Transport He, Jiajun Du, Yuanqi Vargas, Francisco Wang, Yuanqing Gomes, Carla P. Hernández-Lobato, José Miguel Vanden-Eijnden, Eric Machine Learning Chemical Physics Computational Physics We present Free energy Estimators with Adaptive Transport (FEAT), a novel framework for free energy estimation -- a critical challenge across scientific domains. FEAT leverages learned transports implemented via stochastic interpolants and provides consistent, minimum-variance estimators based on escorted Jarzynski equality and controlled Crooks theorem, alongside variational upper and lower bounds on free energy differences. Unifying equilibrium and non-equilibrium methods under a single theoretical framework, FEAT establishes a principled foundation for neural free energy calculations. Experimental validation on toy examples, molecular simulations, and quantum field theory demonstrates improvements over existing learning-based methods. Our PyTorch implementation is available at https://github.com/jiajunhe98/FEAT. |
| title | FEAT: Free energy Estimators with Adaptive Transport |
| topic | Machine Learning Chemical Physics Computational Physics |
| url | https://arxiv.org/abs/2504.11516 |