|About this Abstract
||MS&T21: Materials Science & Technology
||Ceramics and Glasses Modeling by Simulations and Machine Learning
||Bayesian Optimization of Silicon Nitride Empirical Potentials
||Tobias Kroll, Peter Kroll
|On-Site Speaker (Planned)
Engineering classical empirical potentials or force fields to closely match experiment or available quantum-chemically data requires optimization of parameters. Depending on the complexity of the force field, the target properties to be matched, and the available data, this process attains potentially extreme dimensionality associated with large computational costs. Here we apply Bayesian Optimization, a so-called machine learning technique, to optimize empirical potentials used in modeling silicon nitride.
The optimization process uses static and dynamic data generated using Density-Functional-Theory calculations. It offers several hyperparameter options that allow generalization to a variety of empirical potentials.