| About this Abstract |
| Meeting |
2023 TMS Annual Meeting & Exhibition
|
| Symposium
|
Algorithm Development in Materials Science and Engineering
|
| Presentation Title |
Training Machine-learned Interatomic Potentials for Chemical Complexity - Application to Refractory CCAs |
| Author(s) |
Megan J. McCarthy, Jacob Startt, Remi Dingreville, Mitchell Wood |
| On-Site Speaker (Planned) |
Megan J. McCarthy |
| Abstract Scope |
Though machine-learned interatomic potentials (MLIAPs) have greatly improved the accuracy of molecular dynamics, there is still much to be learned in training models for chemical complexity. One important example is found in complex concentrated alloys (CCAs), which contain high concentrations of three or more metallic elements. While excellent progress has been made in generating CCA MLIAPs for single compositions, far less is understood about creating generalized transferable potentials for a range of compositions. This capability is critical to accurate large-scale modeling of CCAs, as chemical complexity can result in large variability in local properties. In this talk, we discuss development of MLIAPs for MoNbTaTi refractory CCAs designed for cross-compositional modeling, using a spectral neighbor analysis potential (SNAP). We describe new ways of quantifying chemical diversity in CCA data sets and explore how it affects mechanical and local-ordering phenomena. SNL is managed and operated by NTESS under DOE NNSA contract DE-NA0003525. |
| Proceedings Inclusion? |
Planned: |
| Keywords |
High-Entropy Alloys, Computational Materials Science & Engineering, Modeling and Simulation |