Machine learning is transforming many scientific fields, including computational materials science. For about two decades, ...

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 ...

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 ...

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 ...

Harvard researchers bring the accuracy, sample efficiency, and robustness of deep equivariant neural networks to the simulate 44 million atoms. This is achieved through a combination of innovative ...