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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 Graph Neural Networks for the Prediction of Metastable Grain Boundary Dynamics
Author(s) Anqi Qiu, Yue Fan
On-Site Speaker (Planned) Anqi Qiu
Abstract Scope Grain boundaries in processed metals often occupy metastable states whose dynamic properties cannot be predicted reliably from equilibrium structures alone. We present a graph-based framework for predicting the dynamics of metastable grain boundaries from atomistic structure. Ensembles of metastable grain boundaries in Fe-Cr-Ni alloys are generated using molecular dynamics, and ART Nouveau is employed to sample local atomic arrangement pathways and calculate their associated activation energy barriers. The activation energy barriers characterize the local energy landscape and provide a physically interpretable intermediate representation between atomic structure and dynamic properties such as aging, rejuvenation, migration, and diffusion. Graph neural networks are trained to predict the activation energy barriers from atomic structures. Their performance is evaluated against descriptor-based baselines, including the smooth overlap of atomic positions (SOAP). This framework establishes a route for predicting grain boundary dynamics from static atomic configurations while retaining a mechanistic connection to the underlying activated processes.
Proceedings Inclusion? Planned:
Keywords Computational Materials Science & Engineering, Iron and Steel,

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