FEAT: Free energy Estimators with Adaptive Transport

Abstract: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 this https URL.

Accepted to NeurIPS 2025; the first two authors contribute equally to this work; add an erratum to Appendix F.4 clarifying the applicability of the TSM loss

Machine Learning (stat.ML); Machine Learning (cs.LG); Chemical Physics (physics.chem-ph); Computational Physics (physics.comp-ph)

arXiv:2504.11516 [stat.ML]

(or arXiv:2504.11516v4 [stat.ML] for this version)