The choice of simulation methods in computational materials science is driven by a
fundamental trade-off: bridging large time-and length-scales with highly accurate …

Unsupervised learning is becoming an essential tool to analyze the increasingly large
amounts of data produced by atomistic and molecular simulations, in material science, solid …

Machine learning (ML) is transforming all areas of science. The complex and time-
consuming calculations in molecular simulations are particularly suitable for an ML …

As the quantum chemistry (QC) community embraces machine learning (ML), the number of
new methods and applications based on the combination of QC and ML is surging. In this …

Molecular dynamics (MD) enables the study of physical systems with excellent
spatiotemporal resolution but suffers from severe timescale limitations. To address this …

Machine learning (ML) has emerged as a formidable force for identifying hidden but
pertinent patterns within a given data set with the objective of subsequent generation of …

Here we propose the reweighted autoencoded variational Bayes for enhanced sampling
(RAVE) method, a new iterative scheme that uses the deep learning framework of variational …

Molecular clusters are ubiquitous constituents of the ambient atmosphere, that can grow into
larger sizes forming new aerosol particles. The formation and growth of small clusters into …

We introduce a general framework for constructing coarse-grained potential models without
ad hoc approximations such as limiting the potential to two-and/or three-body contributions …

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 …