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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 Advancing BEE-NET for Physics-Informed Quantum Materials Discovery
Author(s) Bariana Bowman, Eric Fonseca, Jason Gibson, Ajinkya Hire, Sam Dillon, Benjamin Geisler, Jiabin Yu, Peter Hirschfeld, Richard Hennig
On-Site Speaker (Planned) Bariana Bowman
Abstract Scope Physics-informed graph neural networks (GNNs) are accelerating materials discovery by combining machine learning with domain knowledge. Building on the Bootstrapped Ensemble of Equivariant Graph Neural Networks (BEE-NET), which accurately predicts superconducting properties from crystal structures, we introduce an extended architecture that incorporates three-body interactions and symmetry-aware geometric features. The model expands the original BEE-NET framework, trained to predict the Eliashberg spectral function α²F(ω), into a multi-task architecture capable of simultaneously learning superconducting and geometric descriptors. This unified approach enables interpretable, high-throughput screening of superconducting materials while providing a scalable foundation for learning across diverse crystalline systems. The resulting open-source workflow integrates dataset curation, model development, and large-scale screening to accelerate superconductor discovery. More broadly, the framework is applicable to other quantum materials, enabling efficient exploration of chemical and structural spaces beyond the reach of conventional first-principles methods.
Proceedings Inclusion? Planned:
Keywords Computational Materials Science & Engineering, Machine Learning,

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