| About this Abstract |
| Meeting |
2021 TMS Annual Meeting & Exhibition
|
| Symposium
|
Algorithm Development in Materials Science and Engineering
|
| Presentation Title |
Understanding Grain Boundary Metastability Using the SOAP Descriptor and Unsupervised Machine Learning Techniques |
| Author(s) |
Lydia S H Serafin, Derek Hensley, Jay Spendlove, Gus L W Hart, Eric R. Homer |
| On-Site Speaker (Planned) |
Lydia S H Serafin |
| Abstract Scope |
While it has long been known that grain boundaries (GBs) exhibit a diversity of atomic configurations, our ability to quantify these microscopic configurations and link them to observed properties remains a challenge. Recent work has shown the importance of the metastable GB states in addition to the minimum energy configurations commonly studied. Unfortunately, the added complexity of these configurations makes exploration of this space difficult. We use unsupervised machine learning techniques to examine this space and group GB states into unique clusters, that may correspond to potential energy basins in the GB configuration space. Since annotated datasets of this type are not readily available, this makes validation of the unsupervised machine learning techniques difficult. We will present our efforts to validate these approaches using an annotated dataset, where success of unsupervised machine learning techniques in characterizing GB structure would present an important step forward in grain boundary design. |
| Proceedings Inclusion? |
Planned: |