| About this Abstract |
| Meeting |
2021 TMS Annual Meeting & Exhibition
|
| Symposium
|
Algorithm Development in Materials Science and Engineering
|
| Presentation Title |
Machine Learning and Supercomputing to Accelerate the Development of ReaxFF Interatomic Potentials |
| Author(s) |
Naga Sri Harsha Gunda, Jian Peng, Yun Kyung Shin, Sangkeun Lee, Adri C. T. Van Duin , Dongwon Shin |
| On-Site Speaker (Planned) |
Naga Sri Harsha Gunda |
| Abstract Scope |
We demonstrate a workflow that can significantly accelerate the devolvement of high-fidelity interatomic potentials for atomistic simulations, such as molecular dynamics and reactive force field (ReaxFF). An example we use in this presentation is the development of ReaxFF interatomic potentials of bcc Cr with 18 parameters. We use supercomputing to rapidly populate a large volume of training datasets for parameterization study in the context of machine learning (ML). We start with training multiple ML models that can predict the ReaxFF simulation results in several properties. Then we use Markov Chain Monte Carlo to optimize individual model parameters to replicate temperature- and composition-dependent experimental data. The procedure we have developed will provide an effective methodology that can be applied for traversing a high dimensional space for global optimization of modeling parameters. This research was sponsored by the Department of Energy, Vehicle Technologies Office, Propulsion Materials Program. |
| Proceedings Inclusion? |
Planned: |
| Keywords |
Machine Learning, Modeling and Simulation, Computational Materials Science & Engineering |