Machine learning is transforming many scientific fields, including computational materials science. For about two decades, scientists have been using it to make accurate yet inexpensive calculations ...

Machine learning interatomic potentials (MLIPs) have become an essential tool to enable long-time scale simulations of materials and molecules at unprecedented accuracies. The aim of this collection ...

illustrating the comprehensive zero-shot benchmark of 19 universal machine learning interatomic potentials and the dominant impact of training data composition for surface energy prediction. A ...

Northwestern Engineering researchers have developed a new framework using machine learning that improves the accuracy of interatomic potentials — the guiding rules describing how atoms interact — in ...

Interatomic Potentials and modelling as a tool in materials science – Prof Sir Richard Catlow, Dept. of Chemistry, UCL; School of Chemistry, Cardiff University; UK Catalysis Hub, Research Complex at ...

Catalysts play an indispensable role in modern manufacturing. More than 80% of all manufactured products, from pharmaceuticals to plastics, rely on catalytic processes at some stage of production.