|About this Abstract
||2018 TMS Annual Meeting & Exhibition
||Algorithm Development in Materials Science and Engineering
||Accelerating Materials Simulation by Machine Learning
||Alireza Khorshidi, Andrew Peterson
|On-Site Speaker (Planned)
Electronic-structure calculations such as those employing Kohn-Sham density-functional theory have allowed for accurate atomistic-level simulations in materials science. However, with the currently existing computational facilities, these methods tend to be limited to systems of at most a few hundred atoms, necessitating the need for the development of new computational methods for large length- and time-scale simulations. Machine-learning techniques can provide accurate potentials that can, in principle, match the quality of electronic-structure calculations, provided sufficient training data. In this talk we will review the theory of machine-learning interatomic potentials and the implementation within the open-source Atomistic Machine-learning Package Amp. We then discuss how machine-learning potentials provide particular advantages as the molecular mechanics simulator in the well-known framework of hybrid quantum-mechanics / molecular-mechanics methods.
||Planned: Supplemental Proceedings volume