Machine learning for molecular simulation

Abstract

Machine learning (ML) is transforming all areas of science. The complex and time-consuming calculations in molecular simulations are particularly suitable for a machine learning revolution and have already been profoundly impacted by the application of existing ML methods. Here we review recent ML methods for molecular simulation, with particular focus on (deep) neural networks for the prediction of quantum-mechanical energies and forces, coarse-grained molecular dynamics, the extraction of free energy surfaces and kinetics and generative network approaches to sample molecular equilibrium structures and compute thermodynamics. To explain these methods and illustrate open methodological problems, we review some important principles of molecular physics and describe how they can be incorporated into machine learning structures. Finally, we identify and describe a list of open challenges for the interface between ML and molecular simulation.

References in corpus (10)

• Methods for Interpreting and Understanding Deep Neural Networks
• ANI-1: An extensible neural network potential with DFT accuracy at force field computational cost
• Unmasking Clever Hans Predictors and Assessing What Machines Really Learn
• Extended dynamic mode decomposition with dictionary learning: a data-driven adaptive spectral decomposition of the Koopman operator
• A Variational Approach to Enhanced Sampling and Free Energy Calculations
• ANI-1: A data set of 20M off-equilibrium DFT calculations for organic molecules
• Variational cross-validation of slow dynamical modes in molecular kinetics
• Nonlinear Discovery of Slow Molecular Modes using State-Free Reversible VAMPnets
• Molecular Force Fields with Gradient-Domain Machine Learning: Construction and Application to Dynamics of Small Molecules with Coupled Cluster Forces
• A Universal Density Matrix Functional from Molecular Orbital-Based Machine Learning: Transferability across Organic Molecules