Owing to the central importance of water to life as well as its unusual properties, potentials
for water have been the subject of extensive research over the past 50 years. Recently, five …

The introduction of modern Machine Learning Potentials (MLPs) has led to a paradigm
change in the development of potential energy surfaces for atomistic simulations. By …

Recent theoretical and algorithmic developments have improved the accuracy with which
path integral dynamics methods can include nuclear quantum effects in simulations of …

Abstract Machine learning (ML) based interatomic potentials have transformed the field of
atomistic materials modelling. However, ML potentials depend critically on the quality and …

The development of machine learning models has led to an abundance of datasets
containing quantum mechanical (QM) calculations for molecular and material systems …

Accurate electronic energies and properties are crucial for successful reaction design and
mechanistic investigations. Computing energies and properties of molecular structures has …

Polarizable force fields are pervasive in the fields of computational chemistry and
biochemistry; however, their empirical or semiempirical nature gives them both weaknesses …

We report a scalable Fortran implementation of the phaseless auxiliary-field quantum Monte
Carlo (ph-AFQMC) and demonstrate its excellent performance and beneficial scaling with …

As the most important solvent, water has been at the center of interest since the advent of
computer simulations. While early molecular dynamics and Monte Carlo simulations had to …

The rise of machine learning has greatly influenced the field of computational chemistry and
atomistic molecular dynamics simulations in particular. One of its most exciting prospects is …