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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 Knowledge-Evolution-Informed Forecasting of Mg Alloy Research Using Large Language Models
Author(s) Kelvin Y. Xie
On-Site Speaker (Planned) Kelvin Y. Xie
Abstract Scope The development of structural alloys has historically been driven by the coupled evolution of composition, processing, microstructure, properties, and mechanistic understanding. We hypothesize that these developments contain latent patterns that can be learned by transformer-based AI models. In this work, we construct a structured database from approximately 80 years of Mg and Mg alloy literature, including alloy systems, processing routes, properties, microstructural features, mechanisms, publication year, and citation dynamics. Using this dataset, we explore whether AI can forecast future metallurgical research trajectories and identify weak early signals associated with future breakthrough directions. We will also discuss validation strategies using historical ground truth and the broader implications of AI-assisted forecasting of metallurgical innovation.
Proceedings Inclusion? Planned:
Keywords Machine Learning, Magnesium,

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