Accelerated Sampling of Rare Events using a Neural Network Bias Potential

TL;DR

This paper presents a neural network-based importance sampling method to efficiently simulate rare atomic-scale events, outperforming traditional techniques in accuracy and scalability.

Contribution

It introduces a scalable deep neural network approach to approximate bias potentials, actively learning from successful samples for improved rare event sampling.

Findings

DNN-based importance sampling effectively captures rare events.

Method outperforms traditional Monte Carlo in accuracy.

Scalable to high-dimensional problems.

Abstract

In the field of computational physics and material science, the efficient sampling of rare events occurring at atomic scale is crucial. It aids in understanding mechanisms behind a wide range of important phenomena, including protein folding, conformal changes, chemical reactions and materials diffusion and deformation. Traditional simulation methods, such as Molecular Dynamics and Monte Carlo, often prove inefficient in capturing the timescale of these rare events by brute force. In this paper, we introduce a practical approach by combining the idea of importance sampling with deep neural networks (DNNs) that enhance the sampling of these rare events. In particular, we approximate the variance-free bias potential function with DNNs which is trained to maximize the probability of rare event transition under the importance potential function. This method is easily scalable to high-dimensional problems and provides robust statistical guarantees on the accuracy of the estimated probability of rare event transition. Furthermore, our algorithm can actively generate and learn from any successful samples, which is a novel improvement over existing methods. Using a 2D system as a test bed, we provide comparisons between results obtained from different training strategies, traditional Monte Carlo sampling and numerically solved optimal bias potential function under different temperatures. Our numerical results demonstrate the efficacy of the DNN-based importance sampling of rare events.