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Analyzing Melts and Fluids from Ab Initio Molecular Dynamics Simulations with the UMD Package

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

Linfeng Zhang¹, Han Wang², Weinan E³

¹Program in Applied and Computational Mathematics, Princeton University, Princeton, New Jersey 08544, USA.

The Journal of Chemical Physics

|April 2, 2018

Summary

This study introduces a novel method for molecular simulation, using deep reinforcement learning to efficiently explore configurations and calculate free energy. This approach enhances the study of large atomic and molecular systems.

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Area of Science:

Computational Chemistry
Molecular Dynamics
Machine Learning

Background:

Efficiently exploring configuration space and computing free energy are crucial for understanding large atomic and molecular systems.
Traditional methods face challenges with system size and the selection of collective variables.

Purpose of the Study:

To propose a new approach for efficient exploration of configuration space and free energy computation in large systems.
To leverage deep reinforcement learning for adaptive biasing potentials in molecular dynamics.

Main Methods:

The method combines principles from metadynamics and deep reinforcement learning.
An adaptively computed biasing potential is added to the original dynamics, trained on-the-fly using deep neural networks.
Uncertainty from the neural network model serves as the reward function.

Main Results:

The approach enables efficient exploration of configuration space for large atomic and molecular systems.
Neural network parameterization allows handling a large set of collective variables, reducing the criticality of their precise selection.
Demonstrated effectiveness on full-atom explicit solvent models of alanine dipeptide, tripeptide, and a polyalanine-10 molecule.

Conclusions:

This novel method offers a powerful tool for advancing molecular simulations.
The integration of deep reinforcement learning provides a more robust and flexible approach to free energy calculations.
The technique shows promise for studying complex structural transformations in large molecular systems.