Machine learning potentials represent a transformative bridge between empirical force fields and fully fledged quantum-mechanical simulations, offering near ab initio accuracy at a fraction of the ...

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 ...

Researchers from Carnegie Mellon University and Los Alamos National Laboratory have used machine learning to create a model that can simulate reactive processes in a diverse set of organic materials ...

A simulation demonstrates the reactions that the ANI-1xnr can produce. ANI-1xnr can simulate reactive processes for organic materials, such as as carbon, using less computing power and time than ...