Free energy reconstruction from steered dynamics without post-processing

TL;DR

This paper introduces a Bayesian-based method for direct free energy reconstruction from steered molecular dynamics, eliminating the need for post-processing and improving accuracy in complex systems.

Contribution

It presents a novel Bayesian approach that uses a posterior likelihood to both steer dynamics and infer equilibrium contributions, demonstrated with two different scheduling methods.

Findings

Accurately calculates vacancy migration barrier in Fe-alpha.

Reconstructs 2D free energy landscape of Lennard-Jones cluster.

Eliminates the need for maximum-likelihood post-processing.

Abstract

Various methods achieving importance sampling in ensembles of nonequilibrium trajectories enable to estimate free energy differences and, by maximum-likelihood post-processing, to reconstruct free energy landscapes. Here, based on Bayes theorem, we propose a more direct method in which a posterior likelihood function is used both to construct the steered dynamics and to infer the contribution to equilibrium of all the sampled states. The method is implemented with two steering schedules. First, using non-autonomous steering, we calculate the migration barrier of the vacancy in Fe-alpha. Second, using an autonomous scheduling related to metadynamics and equivalent to temperature-accelerated molecular dynamics, we accurately reconstruct the two-dimensional free energy landscape of the 38-atom Lennard-Jones cluster as a function of an orientational bond-order parameter and energy, down to the solid-solid structural transition temperature of the cluster and without maximum-likelihood post-processing.