https://www.sciencedaily.com/releases/2021/02/210223164437.htm

"This approach promises to be an important building block for the study of materials damage and aging from first principles," said project lead Justin Smith of Los Alamos National Laboratory. "Simulating the dynamics of interacting atoms is a cornerstone of understanding and developing new materials. Machine learning methods are providing computational scientists new tools to accurately and efficiently conduct these atomistic simulations. Machine learning models like this are designed to emulate the results of highly accurate quantum simulations, at a small fraction of the computational cost."

To maximize the general accuracy of these machine learning models, he said, it is essential to design a highly diverse dataset from which to train the model. A challenge is that it is not obvious, a priori, what training data will be most needed by the ML model. The team's recent work presents an automated "active learning" methodology for iteratively building a training dataset.