Free energy calculations: An efficient adaptive biasing potential method

TL;DR

This paper introduces an efficient adaptive biasing potential method for free energy calculations that reduces equilibration time and improves accuracy through mollification and deconvolution, outperforming some existing techniques.

Contribution

The authors develop a novel mollified density of states approach within the ABP framework, enhancing efficiency and simplicity in free energy computations without complex matrix operations.

Findings

Achieves up to tenfold efficiency gain over traditional ABF methods

Provides accurate free energy estimates with controlled mollification errors

Demonstrates comparable or superior performance to well-tempered metadynamics

Abstract

We develop an efficient sampling and free energy calculation technique within the adaptive biasing potential (ABP) framework. By mollifying the density of states we obtain an approximate free energy and an adaptive bias potential that is computed directly from the population along the coordinates of the free energy. Because of the mollifier, the bias potential is "nonlocal" and its gradient admits a simple analytic expression. A single observation of the reaction coordinate can thus be used to update the approximate free energy at every point within a neighborhood of the observation. This greatly reduces the equilibration time of the adaptive bias potential. This approximation introduces two parameters: strength of mollification and the zero of energy of the bias potential. While we observe that the approximate free energy is a very good estimate of the actual free energy for a large range of mollification strength, we demonstrate that the errors associated with the mollification may be removed via deconvolution. The zero of energy of the bias potential, which is easy to choose, influences the speed of convergence but not the limiting accuracy. This method is simple to apply to free energy or mean force computation in multiple dimensions and does not involve second derivatives of the reaction coordinates, matrix manipulations nor on-the-fly adaptation of parameters. For the alanine dipeptide test case, the new method is found to gain as much as a factor of ten in efficiency as compared to two common adaptive biasing force formulations and it is shown to be as efficient as well-tempered metadynamics with the post-process deconvolution giving a clear advantage to the mollified density of states method.