| About this Abstract |
| Meeting |
2025 TMS Annual Meeting & Exhibition
|
| Symposium
|
Structure and Dynamics of Metallic Glasses
|
| Presentation Title |
Predicting orientation-dependent plastic susceptibility from static structure in amorphous solids via convolutional neural networks |
| Author(s) |
Zhao Fan, Evan Ma |
| On-Site Speaker (Planned) |
Zhao Fan |
| Abstract Scope |
It has been a long-standing challenge to establish structure-property relations in amorphous solids. Here we introduce a rotationally non-invariant local structure representation that enables different predictions for different loading orientations, which is found essential for high-fidelity prediction of the propensity for stress-driven shear transformations. This novel structure representation, when combined with convolutional neural networks, leads to unprecedented accuracy for identifying atoms with high propensity for stress-driven shear transformations, solely from the static structure in both two- and three-dimensional model glasses. The data-driven models trained on samples at one composition and a given processing history are found transferrable to glass samples with different processing histories or at different compositions in the same alloy system. Our analysis of the new structure representation also provides valuable insight into key atomic packing features that influence the local mechanical response and its anisotropy in glasses. This work was published in [Nat. Commun. 12, 1506 (2021)]. |
| Proceedings Inclusion? |
Planned: |
| Keywords |
Machine Learning, Modeling and Simulation, Mechanical Properties |