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 ...

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 ...

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 ...