# Reinforced dynamics for enhanced sampling in large atomic and molecular systems

arXiv: https://arxiv.org/abs/1712.03461 · HTML: https://mlsanity.com/paper/1712.03461/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Dec 2017.

## Built on by (most cited first, 7 of 7)

- [Pandemic Drugs at Pandemic Speed: Infrastructure for Accelerating COVID-19 Drug Discovery with Hybrid Machine Learning- and Physics-based Simulations on High Performance Computers](https://mlsanity.com/paper/2103.02843/) (2021)
- [Enhanced Sampling for Efficient Learning of Coarse-Grained Machine Learning Potentials](https://mlsanity.com/paper/2510.11148/) (2025)
- [Active Learning for Saddle Point Calculation](https://mlsanity.com/paper/2108.04698/) (2021)
- [Accelerated Sampling of Rare Events using a Neural Network Bias Potential](https://mlsanity.com/paper/2401.06936/) (2024)
- [Enhanced Sampling with Machine Learning: A Review](https://mlsanity.com/paper/2306.09111/) (2023)
- [Unbiasing Enhanced Sampling on a High-dimensional Free Energy Surface with Deep Generative Model](https://mlsanity.com/paper/2312.09404/) (2023)
- [Consensus-based adaptive sampling and approximation for high-dimensional energy landscapes](https://mlsanity.com/paper/2311.05009/) (2023)

## Abstract (arXiv)

A new approach for efficiently exploring the configuration space and computing the free energy of large atomic and molecular systems is proposed, motivated by an analogy with reinforcement learning. There are two major components in this new approach. Like metadynamics, it allows for an efficient exploration of the configuration space by adding an adaptively computed biasing potential to the original dynamics. Like deep reinforcement learning, this biasing potential is trained on the fly using deep neural networks, with data collected judiciously from the exploration and an uncertainty indicator from the neural network model playing the role of the reward function. Parameterization using neural networks makes it feasible to handle cases with a large set of collective variables. This has the potential advantage that selecting precisely the right set of collective variables has now become less critical for capturing the structural transformations of the system. The method is illustrated by studying the full-atom, explicit solvent models of alanine dipeptide and tripeptide, as well as the system of a polyalanine-10 molecule with 20 collective variables.