Learning Free Energy Landscapes Using Artificial Neural Networks

TL;DR

This paper introduces a neural network-based adaptive biasing method for efficiently learning complex free energy landscapes in molecular simulations, overcoming limitations of traditional fixed-kernel techniques.

Contribution

It presents a novel neural network approach with Bayesian regularization for rapid, robust free energy landscape estimation requiring minimal user intervention.

Findings

Rapid adaptation to complex landscapes

Reduced boundary and oscillation issues

Less time-consuming than conventional methods

Abstract

Existing adaptive bias techniques, which seek to estimate free energies and physical properties from molecular simulations, are limited by their reliance on fixed kernels or basis sets which hinder their ability to efficiently conform to varied free energy landscapes. Further, user-specified parameters are in general non-intuitive, yet significantly affect the convergence rate and accuracy of the free energy estimate. Here we propose a novel method wherein artificial neural networks (ANNs) are used to develop an adaptive biasing potential which learns free energy landscapes. We demonstrate that this method is capable of rapidly adapting to complex free energy landscapes and is not prone to boundary or oscillation problems. The method is made robust to hyperparameters and overfitting through Bayesian regularization which penalizes network weights and auto-regulates the number of effective parameters in the network. ANN sampling represents a promising innovative approach which can resolve complex free energy landscapes in less time than conventional approaches while requiring minimal user input.