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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 Physics-Informed Graph Neural Networks for Predicting Li-Ion Transport in Solid Electrolyte Interphases
Author(s) Yanqing Su
On-Site Speaker (Planned) Yanqing Su
Abstract Scope Understanding Li-ion transport through the solid electrolyte interphase (SEI) is critical for improving the performance and safety of lithium-metal batteries. This work presents a physics-informed machine learning framework that combines density functional theory (DFT) nudged elastic band calculations with graph neural networks (GNNs) to predict Li-ion migration behavior in inorganic SEI materials. A diffusion dataset was constructed for eight major SEI components, including LiF, LiCl, LiBr, LiI, Li₂O, Li₂S, Li₃N, and Li₂CO₃, covering both grain and grain-boundary diffusion pathways. A path-aware graph variational autoencoder was used to learn latent representations of migration trajectories, and the resulting embeddings were incorporated into a predictive GNN model for migration barrier estimation. The framework achieved test-set Rē values above 0.93 for both grain and grain-boundary diffusion. Feature and latent-space analyses reveal how SEI chemistry and local interfacial structure jointly govern ion transport, enabling AI-assisted discovery and design of high-performance battery interphases.
Proceedings Inclusion? Planned:
Keywords Energy Conversion and Storage, Machine Learning, Modeling and Simulation

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