Targeted free energy perturbation revisited: Accurate free energies from mapped reference potentials

TL;DR

This paper introduces an improved targeted free energy perturbation method that uses neural network-based mappings to efficiently and accurately compute free energy differences at quantum mechanical levels from cheaper reference potentials.

Contribution

It extends TFEP theory by integrating normalizing flow neural networks to learn mappings, reducing systematic errors and avoiding extensive simulations with expensive target potentials.

Findings

Accurately computes free energy differences in double-well systems.

Successfully describes free energy landscape of a simple gas-phase chemical reaction.

Reduces systematic errors compared to previous TFEP approaches.

Abstract

We present an approach that extends the theory of targeted free energy perturbation (TFEP) to calculate free energy differences and free energy surfaces at an accurate quantum mechanical level of theory from a cheaper reference potential. The convergence is accelerated by a mapping function that increases the overlap between the target and the reference distributions. Building on recent work, we show that this map can be learned with a normalizing flow neural network, without requiring simulations with the expensive target potential but only a small number of single-point calculations, and, crucially, avoiding the systematic error that was found previously. We validate the method by numerically evaluating the free energy difference in a system with a double-well potential and by describing the free energy landscape of a simple chemical reaction in the gas phase.