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

Keywords: 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.

Primary Area: Probabilistic methods (e.g., variational inference, causal inference, Gaussian processes)

Submission Number: 9569