About this Abstract |
Meeting |
2023 TMS Annual Meeting & Exhibition
|
Symposium
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AI/Data Informatics: Computational Model Development, Validation, and Uncertainty Quantification
|
Presentation Title |
Inferring Topological Transitions in Pattern-forming Processes via Self-supervised Learning |
Author(s) |
Marcin Abram, Keith Burghardt, Greg Ver Steeg, Remi Dingreville |
On-Site Speaker (Planned) |
Remi Dingreville |
Abstract Scope |
The identification of transitions in microstructural regimes in pattern-forming processes is critical for understanding and fabricating microstructurally precise materials. Motivated by the universality principle for dynamical systems, we use a self-supervised approach to solve the inverse problem of predicting process parameters from observed microstructures using neural networks. We show that the difficulty of performing this prediction task is related to the goal of discovering transitions in microstructure regimes, because qualitative changes in microstructural patterns correspond to changes in uncertainty for our prediction problem. We demonstrate our approach for two distinct pattern-forming processes: the spinodal decomposition of a two-phase mixture and the formation of concentration modulations of binary alloys during physical vapor deposition of thin films. This approach opens a promising path forward for discovering unseen or hard-to-detect transition regimes. SNL is managed and operated by NTESS under DOE NNSA contract DE-NA0003525. SAND2022-3365 A |
Proceedings Inclusion? |
Planned: |
Keywords |
Machine Learning, Phase Transformations, Computational Materials Science & Engineering |