FEAT: Free energy Estimators with Adaptive Transport
Yuanqi Du, Jiajun He, Francisco Vargas, Yuanqing Wang, Carla P Gomes, José Miguel Hernández-Lobato, Eric Vanden-Eijnden
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 promising improvements over existing learning-based methods. Our PyTorch implementation is available at https://github.com/jiajunhe98/FEAT.
BibTeX
@inproceedings{
du2025feat,
title={{FEAT}: Free energy Estimators with Adaptive Transport},
author={Yuanqi Du and Jiajun He and Francisco Vargas and Yuanqing Wang and Carla P Gomes and Jos{\'e} Miguel Hern{\'a}ndez-Lobato and Eric Vanden-Eijnden},
booktitle={The Thirty-ninth Annual Conference on Neural Information Processing Systems},
year={2025},
url={https://openreview.net/forum?id=GQXeLGYMda}
}