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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 Dis-GNN: Crystal Graph Neural Network for Disordered Crystal Classification and Site Occupancy Prediction
Author(s) Sam Dong, Pawan Prakash , Richard Hennig
On-Site Speaker (Planned) Sam Dong
Abstract Scope The inverse design of functional materials is an unsolved problem that has the potential to evoke massive technological advances. Over the past few years, the accelerated progression of high fidelity machine learning models has led to a dramatic rise in efficiency in the generation/screening of inorganic crystals with targeted properties. However, a substantial gap still remains between predictions made by machine learning models for materials and what is tangibly observed through experiment. Crystallographic disorder is one aspect that contributes to this gap. We introduce dis-GNN, a crystal-graph convolution neural network for disordered crystal classification, achieving a hold-out accuracy of 92%. We further extend predictions to individual atomic sites, achieving a hold-out accuracy of 99% on atom-level classification, as well as a MAE of 2.5% and R2 OF 0.85 for partial site occupancies.
Proceedings Inclusion? Planned:
Keywords Machine Learning,

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