# Neural networks-based variationally enhanced sampling

## TL;DR

This paper introduces a neural network-based approach to variationally enhanced sampling, improving the efficiency of sampling complex free energy surfaces in atomistic simulations by leveraging machine learning and reinforcement learning techniques.

## Contribution

It develops a neural network implementation of the bias potential in variationally enhanced sampling, enabling better handling of complex free energy landscapes and multiple collective variables.

## Key findings

- Accelerates sampling of complex free energy surfaces.
- Reduces boundary effects artifacts.
- Handles multiple collective variables more effectively.

## Abstract

Sampling complex free energy surfaces is one of the main challenges of modern atomistic simulation methods. The presence of kinetic bottlenecks in such surfaces often renders a direct approach useless. A popular strategy is to identify a small number of key collective variables and to introduce a bias potential that is able to favor their fluctuations in order to accelerate sampling. Here we propose to use machine learning techniques in conjunction with the recent variationally enhanced sampling method [Valsson and Parrinello, Physical Review Letters 2014] to determine such potential. This is achieved by expressing the bias as a neural network. The parameters are determined in a reinforcement learning scheme aimed at minimizing the variationally enhanced sampling functional. This required the development of a new and more efficient minimization technique. The expressivity of neural networks allows accelerating sampling in systems with rapidly varying free energy surfaces, removing boundary effects artifacts, and making one more step towards being able to handle several collective variables.