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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 Network Prediction of Grain-Boundary Diffusion from a Reduced Active-Atom Subset
Author(s) Sohrab Salimi Bani, Panthea Sepehrband, Morad Behandish
On-Site Speaker (Planned) Sohrab Salimi Bani
Abstract Scope Grain boundary diffusion arises from localized atomic rearrangements near boundaries, but molecular dynamics(MD) becomes costly at long times because all atoms must be propagated. We develop a coordination-guided graph neural network(GNN) for a low-angle Al twist boundary. MD provided per-atom displacement and coordination number on the boundary plane. K-means clustering of mean absolute coordination-number change classified 43% of all atoms as active; these atoms became nodes in a radius graph, while weakly mobile atoms were kept as a frozen baseline. The GNN was trained as a one-step forecaster, using 1–2 ns active-atom features to predict 2–3 ns mean squared displacement and coordination evolution, then rolled forward through 5 ns. The one-step slope error was 2.02%, and the total rollout error was 3.05%. Forecasting via GNN required <1 s on a commodity GPU, while MD required 64 h for the same period, even on a 48-core CPU. Transferability remains future work.
Proceedings Inclusion? Planned:
Keywords Machine Learning, Modeling and Simulation,

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