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

University of Cambridge· Cornell University· Xaira Therapeutics· Capital Fund Management· New York University

escorted Jarzynski equality free energy calculation stochastic differential equation Monte Carlo method

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.