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 https://github.com/jiajunhe98/FEAT.
Keywords
Cite
@article{arxiv.2504.11516,
title = {FEAT: Free energy Estimators with Adaptive Transport},
author = {Jiajun He and Yuanqi Du and Francisco Vargas and Yuanqing Wang and Carla P. Gomes and José Miguel Hernández-Lobato and Eric Vanden-Eijnden},
journal= {arXiv preprint arXiv:2504.11516},
year = {2026}
}
Comments
Accepted to NeurIPS 2025; the first two authors contribute equally to this work