In this review, we highlight recent developments in the application of machine learning for
molecular modeling and simulation. After giving a brief overview of the foundations …

ChemML is an open machine learning (ML) and informatics program suite that is designed
to support and advance the data‐driven research paradigm that is currently emerging in the …

The freud Python package is a library for analyzing simulation data. Written with modern
simulation and data analysis workflows in mind, freud provides a Python interface to fast …

Classical molecular dynamics simulates the time evolution of molecular systems through the
phase space spanned by the positions and velocities of the constituent atoms. Molecular …

The success of enhanced sampling molecular simulations that accelerate along collective
variables (CVs) is predicated on the availability of variables coincident with the slow …

We introduce a free and open-source software package (PES-Learn) which largely
automates the process of producing high-quality machine learning models of molecular …

Understanding heat transport in semiconductors and insulators is of fundamental
importance because of its technological impact in electronics and renewable energy …

Small integration time steps limit molecular dynamics (MD) simulations to millisecond time
scales. Markov state models (MSMs) and equation-free approaches learn low-dimensional …

State-free reversible VAMPnets (SRVs) are a neural network-based framework capable of
learning the leading eigenfunctions of the transfer operator of a dynamical system from …

The use of theory and simulation in undergraduate education in biochemistry, molecular
biology, and structural biology is now common, but the skills students need and the …