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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

Harmonized Data Schema for AI Ready Materials R&D Data

Author(s)

Kwang-Ryeol Lee

On-Site Speaker (Planned)

Kwang-Ryeol Lee

Abstract Scope

For materials R&D data to truly empower AI-driven design, two fundamental characteristics are crucial. First, datasets must offer sufficient context, explicitly linking a material's structure, composition, properties, and performance. Without this crucial contextual information, the data's value for developing robust AI models significantly diminishes. Second, the data infrastructure demands harmonization. Expressing R&D data across all materials science sub-disciplines—from traditional steel to advanced functional materials—within a consistent schema would dramatically enhance its utility. While this presents a significant challenge due to field diversity, our approach separates inherent physical/chemical data from application-specific data and employs a "materials system" concept for logical consistency. A standardized data schema is under continuous development, with semi-annual releases available on GitHub (https://github.com/NCMRD/Standard-Expert-Committee). This effort is key to unlocking the full potential of materials data in design innovation.

Proceedings Inclusion?

Planned:

Keywords

ICME, Computational Materials Science & Engineering, Other

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Bayesian Inference of Local SiC/SiC Thermal Conductivity with Curved Geometry

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