Combining electronic structure calculations and machine learning (ML) techniques has become a common approach in the atomistic modeling of matter. Using the two techniques together has allowed ...

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 ...

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 ...