| About this Abstract |
| Meeting |
2024 TMS Annual Meeting & Exhibition
|
| Symposium
|
Dynamic Behavior of Materials X
|
| Presentation Title |
Development of an ML Interatomic Potential for SiC for Extreme Environments |
| Author(s) |
Michael Paul MacIsaac, Salil Bavdekar, Douglas Spearot, Ghatu Subhash |
| On-Site Speaker (Planned) |
Michael Paul MacIsaac |
| Abstract Scope |
A linear regression-based machine learned interatomic potential (MLIP) was developed for the silicon-carbon system. The MLIP was predominantly trained on structures discovered through a genetic algorithm, encompassing the entire silicon-carbon composition space. To improve MLIP performance on mechanical properties, the linear regression learning algorithm was modified to include higher spline interpolation resolution in regions with large potential energy surface curvature. The developed MLIP demonstrates exceptional predictive performance, accurately estimating energies and forces for structures across the silicon-carbon composition space. It predicts mechanical properties of SiC with high precision and captures fundamental volume-pressure and volume-temperature relationships. Uniquely, this silicon-carbon MLIP is adept at modeling complex high-temperature phenomena, including the peritectic decomposition of SiC and carbon dimer formation during SiC surface reconstruction, which cannot be captured with prior classical interatomic potentials for this material system. |
| Proceedings Inclusion? |
Planned: |