Multiscale Modeling of Defect Phenomena in Platinum Using Machine Learning of Force Fields

Abstract

Computational methodologies have been critical to our understanding of defects at nanometer scales. These methodologies have been dominated by two classes: quantum mechanics (QM)-based methods and semiempirical/classical methods. The former, while accurate and versatile, are time consuming, while the latter are efficient but limited in versatility and transferability. Recently, machine learning (ML) methods have shown initial promise in bridging these two limitations due to their accuracy and flexibility. In this work, the true capability of ML methods is explored by simulating defects in platinum over several length/time scales. We compare our results with density functional theory (DFT) for atomic-level defect behavior and with experiments for nanolevel behavior. We also compare our predictions with several classical potentials. This work aims to showcase the length/time scales attainable using ML, as well as the complexity they are capable of capturing, demonstrating that these methodologies may be effectively used, in the future, to bridge experiments and QM methods.