| About this Abstract |
| Meeting |
2020 TMS Annual Meeting & Exhibition
|
| Symposium
|
Algorithm Development in Materials Science and Engineering
|
| Presentation Title |
Scale Bridging from DFT to MD with Machine Learning |
| Author(s) |
Mitchell Wood, Mary Alice Cusentino, Aidan Thompson |
| On-Site Speaker (Planned) |
Mitchell Wood |
| Abstract Scope |
The abundance of data generated from electronic structure calculations lends itself as an excellent starting point for machine learned interatomic potentials(ML-IAP) used in classical Molecular Dynamics(MD) simulations. Unlike empirical potentials used within MD, ML-IAP are capable to express a tunable accuracy with respect to the higher fidelity training data, this of course comes at a greater computational cost. In this talk recent advances in the atomic environment descriptors, model form and computational implementation of the Spectral Neighborhood Analysis Potential (SNAP)will be presented. Each of these developments has been shown to improve the overall accuracy or performance (or both) on modern supercomputing systems. Applications of SNAP ML-IAP to materials in extremes of pressure and radiation damage will be discussed as well. |
| Proceedings Inclusion? |
Planned: Supplemental Proceedings volume |