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 ...

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 ...

The interatomic potential describes the interaction between a pair of atoms or the interaction of an atom with a group of atoms in a condensed phase. The potential must have both an attractive and a ...

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 ...

In a 'hollow atom', electrons occupy high-energy states far away from the nucleus, it can get rid of their excess energy on a remarkably short timescale. The reason for this has been unknown.