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Meeting 2027 TMS Annual Meeting & Exhibition
Symposium AI/ML/Data Informatics for Materials Discovery: Bridging Experiment, Theory, and Modeling
Presentation Title Data-driven inference of twin network dynamics: nucleation, co-nucleation, transmission, and incidental contacts
Author(s) Duncan A. Greeley, Paul Agbaje, Hi Vo, Laurent Capolungo
On-Site Speaker (Planned) Duncan A. Greeley
Abstract Scope Deformation twinning is key mechanism for accommodating plasticity in a wide range of structural alloys, and as deformation progresses, twins interact to form complex domain networks. The topology of these networks is expected to mediate the mechanical response of the microstructure, but to date our understanding of network evolution is limited due to the interrelated mechanisms involved in network evolution (e.g. nucleation, transmission, incidental contacts). To address this, we introduce a machine learning framework to predict the outcome to twin-grain boundary interactions in Ti using multilayer perceptrons. We then leverage the trained model to virtually generate fingerprints of twin networks and explore the role of different mechanisms on the evolution of the network structure. The model predicts contact formation with high accuracy and reveals that geometric alignment and internal stresses must be included to identify contacts in poorly aligned configurations. Further, incidental interactions are observed to notably dictate network morphology.
Proceedings Inclusion? Planned:

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