|About this Abstract
||MS&T21: Materials Science & Technology
||AI for Big Data Problems in Advanced Imaging, Materials Modeling and Automated Synthesis
||Refinements to the Production of Machine Learning Interatomic Potentials
||Jared Stimac, Jeremy Mason
|On-Site Speaker (Planned)
Machine learning potentials (MLP) have the potential to allow dramatically accelerated simulations of atomic systems with the accuracy of quantum mechanical techniques through the use of supervised regression algorithms. The price to be paid is that MLPs have a higher computational expense than empirical potentials, both during construction and for every evaluation of the potential energy. In pursuit of reducing these costs and alleviating the necessity for enormous datasets, our framework for producing MLPs combines an efficient implementation of a sparse Gaussian process algorithm with a novel set of descriptors for atomic environments. It is intended that the descriptors be an injective embedding that imposes minimal distortion and that the sparse Gaussian process selects as few inducing points—which dominate the computational complexity in all respects—as necessary. To this end, we aim to produce better performing potentials with less training and data than competing frameworks.