| About this Abstract |
| Meeting |
2021 TMS Annual Meeting & Exhibition
|
| Symposium
|
Algorithm Development in Materials Science and Engineering
|
| Presentation Title |
Application of a Shape Moment Descriptor Set Towards a Robust and Transferable Description of Local Atomic Environments |
| Author(s) |
Jacob P. Tavenner, Edward M. Kober, Garritt J. Tucker |
| On-Site Speaker (Planned) |
Jacob P. Tavenner |
| Abstract Scope |
In the study of atomistic behavior, mathematical descriptions of atomic structure are critical for robust scientific analysis of both static and dynamic systems. A robust descriptor which improves upon prior methods, requiring no a priori knowledge of the system being analyzed, has been developed. In evidence of the improved performance of these descriptors, a small number of potential applications will be examined. These areas include grain boundary structure, atomic motion, and segregation potential, among others. Improvement of current understanding or analysis methods will be demonstrated using these novel descriptors of local atomic environments. By leveraging these descriptors, the relationship between atomic environments and their underlying physics which drive system behavior can be better understood. Machine learning techniques are utilized to elucidate these complex relationships, demonstrating the applicability of this approach with modern data-driven techniques for processing the substantial volume of data generated through many modern computational studies.
LA-UR-20-25125 |
| Proceedings Inclusion? |
Planned: |
| Keywords |
Computational Materials Science & Engineering, Machine Learning, |