Fast and accurate multidimensional free energy integration

TL;DR

This paper introduces a flexible Poisson equation-based algorithm for integrating multidimensional free energy surfaces from gradient estimates, enhancing the analysis of enhanced sampling simulations in computational chemistry.

Contribution

It presents a portable C++ implementation for multidimensional free energy integration from gradients, compatible with various algorithms and boundary conditions, and integrates with ABF methods.

Findings

pABF accelerates convergence in simple cases

Helmholtz decomposition reduces gradient noise

Variance reduction may hinder exploration in complex systems

Abstract

Enhanced sampling and free energy calculation algorithms of the Thermodynamic Integration family (such as the Adaptive Biasing Force method, ABF) are not based on the direct computation of a free energy surface, but rather of its gradient. Integrating the free energy surface is non-trivial in dimension higher than one. Here the author introduces a flexible, portable implementation of a Poisson equation formalism to integrate free energy surfaces from estimated gradients in dimension 2 and 3, using any combination of periodic and non-periodic (Neumann) boundary conditions. The algorithm is implemented in portable C++, and provided as a standalone tool that can be used to integrate multidimensional gradient fields estimated on a grid using any algorithm, such as Umbrella Integration as a post-treatment of Umbrella Sampling simulations. It is also included in the implementation of ABF (and its extended-system variant eABF) in the Collective Variables Module, enabling the seamless computation of multidimensional free energy surfaces within ABF and eABF simulations. A Python-based analysis toolchain is provided to easily plot and analyze multidimensional ABF simulation results, including metrics to assess their convergence. The Poisson integration algorithm can also be used to perform Helmholtz decomposition of noisy gradients estimates on the fly, resulting in an efficient implementation of the projected ABF (pABF) method proposed by Leli\`evre and co-workers. In numerical tests, pABF is found to lead to faster convergence with respect to ABF in simple cases of low intrinsic dimension, but seems detrimental to convergence in a more realistic case involving degenerate coordinates and hidden barriers, due to slower exploration. This suggests that variance reduction schemes do not always yield convergence improvements when applied to enhanced sampling methods.