| About this Abstract |
| Meeting |
2022 TMS Annual Meeting & Exhibition
|
| Symposium
|
Algorithm Development in Materials Science and Engineering
|
| Presentation Title |
Refinements to the Production of Machine Learning Interatomic Potentials |
| Author(s) |
Jared Stimac, Jeremy Mason |
| On-Site Speaker (Planned) |
Jared Stimac |
| Abstract Scope |
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. One of the related open questions is how to optimally construct the MLP's training set, since expanding the training set increases the computational cost of both MLP construction and potential energy or force evaluations. In pursuit of reducing these costs and alleviating the necessity for enormous training sets, our framework combines an efficient implementation of a sparse Gaussian process algorithm with a novel set of descriptors for atomic environments. These are specifically designed to help the sparse Gaussian process select 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. |
| Proceedings Inclusion? |
Planned: |
| Keywords |
Computational Materials Science & Engineering, Machine Learning, |