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 ...

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 ...

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 ...