The field of data-driven chemistry is undergoing an evolution, driven by innovations in
machine learning models for predicting molecular properties and behavior. Recent strides in …

In the next half-century, physical chemistry will likely undergo a profound transformation,
driven predominantly by the combination of recent advances in quantum chemistry and …

Unleashing the predictive power of molecular dynamics (MD), Neural Network Potentials
(NNPs) trained on Density Functional Theory (DFT) calculations are revolutionizing our …

Advances in nuclear power reactors include the use of mixed oxide fuel, containing uranium
and plutonium oxides. The high-temperature behaviour and structure of PuO2–x above …

Welcome to the Journal of Chemical Physics' Special Topic on Software for Atomistic
Machine Learning. For some years now, search engines have been dominating our online …

In this work, we developed a compositionally transferable machine learning interatomic
potential using atomic cluster expansion potential and PBE-D3 method for (NaCl) 1-x (MgCl …

Molten salts are promising candidates in numerous clean energy applications, where
knowledge of thermophysical properties and vapor pressure across their operating …

Molten salts could play an important role in energy storage, in the form of liquid batteries,
and heat storage for solar and nuclear power. However, their widespread application is …

Electrolyte solutions play critical role in a vast range of important applications, yet an
accurate and scalable method of predicting their properties without fitting to experiment has …

Bridging the gap between diffuse x-ray or neutron scattering measurements and predicted
structures derived from atom-atom pair potentials in disordered materials, has been a …