|About this Abstract
||2020 TMS Annual Meeting & Exhibition
||Algorithm Development in Materials Science and Engineering
||Physically-Motivated Requirements of Machine Learning Potentials
||Jared Stimac, Jeremy K. Mason
|On-Site Speaker (Planned)
Machine learning potentials (MLPs) for molecular dynamics simulations have been found to be capable of approaching ab initio accuracy with the computational efficiency of empirical potentials. The accuracy and performance of an MLP is highly dependent upon the choice of descriptors used to characterize the local atomic environment though, as well as on the training data that the algorithm uses to predict the energies and forces. This work uses Gaussian process regression and investigates the effect of the choice of descriptors and the covariance function on the accuracy of the resulting potential. The descriptors should respect the symmetries of the atomic environment, be differentiable with respect to the atomic coordinates, and contain sufficient information. The covariance function should impose minimal constraints on the potential to reduce the risk of systematic error. A MLP for bulk copper is considered for specificity.
||Planned: Supplemental Proceedings volume