Machine Learning of Interatomic Potentials

Presented by Kipton Barros, Los Alamos National Laboratory

Machine learning is emerging as a powerful tool to emulate electronic structure calculations. Deep neural networks can now predict atomic interactions with accuracies exceeding density functional theory, and approaching that of coupled cluster theory, at a tiny fraction of the computational cost. I will discuss recent methods for building interatomic potentials relevant to chemistry, materials science, and biophysics applications. A key idea is active learning, in which the training dataset is generated on-the-fly, to fill in gaps of the machine learning model, and to achieve a surprising level of transferability.