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Meeting

2026 TMS Annual Meeting & Exhibition

Symposium

AI/ML/Data Informatics for Materials Discovery: Bridging Experiment, Theory, and Modeling

Presentation Title

Evaluation of Out-of-Distribution Errors in Equivariant Graph Neural Networks for Materials Discovery

Author(s)

Peter Schindler

On-Site Speaker (Planned)

Peter Schindler

Abstract Scope

Equivariant graph neural networks (E3GNNs), which enforce the symmetries of three-dimensional Euclidean space, have shown strong performance in predicting materials properties. By directly embedding physical symmetries into their architecture, E3GNNs generate more expressive features, promising improved generalizability while requiring less training data compared to invariant models. The out-of-distribution (OOD) generalization error serves as a valuable metric for assessing a model's true ability to generalize to unseen data. Recently, we have established an open-source Python toolkit, MatFold, which automates the construction of chemical and structural data splits, facilitating OOD generalization evaluation for the materials informatics community. Here, I will present recent work employing MatFold to analyze the generalization error of E3GNN models trained on our surface property dataset and various benchmarking datasets. Specifically, I will compare the OOD generalization performance and its dataset size dependence of the equivariant and invariant models for various chemical and structural holdouts of increasing difficulty.

Proceedings Inclusion?

Planned:

Keywords

Computational Materials Science & Engineering, Machine Learning, Modeling and Simulation

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