Computational study of molecules and materials from first principles is a cornerstone of
physics, chemistry, and materials science, but limited by the cost of accurate and precise …

Graph convolution is the core of most Graph Neural Networks (GNNs) and usually
approximated by message passing between direct (one-hop) neighbors. In this work, we …

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 …

The use of supervised machine learning to develop fast and accurate interatomic potential
models is transforming molecular and materials research by greatly accelerating atomic …

Molecular dynamics (MD) simulations employing classical force fields constitute the
cornerstone of contemporary atomistic modeling in chemistry, biology, and materials …

We present an optimized implementation of the recently proposed symmetric gradient
domain machine learning (sGDML) model. The sGDML model is able to faithfully reproduce …

Surface charge controls many static and dynamic properties of soft matter and
micro/nanofluidic systems, but its unambiguous measurement forms a challenge. Standard …

We present the construction of molecular force fields for small molecules (less than 25
atoms) using the recently developed symmetrized gradient-domain machine learning …

Modern machine learning force fields (ML-FF) are able to yield energy and force predictions
at the accuracy of high-level ab initio methods, but at a much lower computational cost. On …

Dynamics of flexible molecules are often determined by an interplay between local chemical
bond fluctuations and conformational changes driven by long-range electrostatics and van …