Reinforced dynamics for enhanced sampling in large atomic and molecular systems

Abstract

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.

References in corpus (3)

• Canonical sampling through velocity-rescaling
• Well-Tempered Metadynamics: A Smoothly Converging and Tunable Free-Energy Method
• Single-Sweep Methods for Free Energy Calculations

Cited by in corpus (20)

• Active Learning of Uniformly Accurate Inter-atomic Potentials for Materials Simulation
• Solving Many-Electron Schrödinger Equation Using Deep Neural Networks
• Deep Potentials for Materials Science
• DeePCG: constructing coarse-grained models via deep neural networks
• Neural networks-based variationally enhanced sampling
• Deep neural network for the dielectric response of insulators
• Deep learning inter-atomic potential model for accurate irradiation damage simulations
• Efficient sampling of high-dimensional free energy landscapes using adaptive reinforced dynamics
• A Perspective on Deep Learning for Molecular Modeling and Simulations
• Deep Ensembles vs. Committees for Uncertainty Estimation in Neural-Network Force Fields: Comparison and Application to Active Learning
• Enhanced Sampling in the Age of Machine Learning: Algorithms and Applications
• Pushing the limit of molecular dynamics with ab initio accuracy to 100 million atoms with machine learning
• Neural Canonical Transformation with Symplectic Flows
• Integrating Machine Learning with Physics-Based Modeling
• Transition path theory for Langevin dynamics on manifold: optimal control and data-driven solver
• Ab Initio Generalized Langevin Equation
• Machine learning assisted coarse-grained molecular dynamics modeling of meso-scale interfacial fluids
• Pandemic Drugs at Pandemic Speed: Infrastructure for Accelerating COVID-19 Drug Discovery with Hybrid Machine Learning- and Physics-based Simulations on High Performance Computers
• Adaptive tensor train metadynamics for high-dimensional free energy exploration
• Progressive Optimal Path Sampling for Closed-Loop Optimal Control Design with Deep Neural Networks