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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

Towards FAIR-er crystallographic texture data to enable machine learning approaches

Author(s)

Benjamin A. Begley, Victoria M. Miller

On-Site Speaker (Planned)

Benjamin A. Begley

Abstract Scope

Crystallographic texture data is a rich resource for understanding the mechanical behavior of materials; however, the typical forms in which it is published—e.g. pole figures—do not satisfy the FAIR data guidelines. When published only as an image, its reusability is limited; the compatibility of proprietary and open-source file formats for raw texture data strains interoperability. This talk presents an expansion of DRAGON, software for extracting quantitative data from published images of pole figures, to include a computer vision approach to extract data from the arbitrary color gradients of modern pole figures in addition to the original approach for contour lines. In addition, this talk addresses guidelines for publishing crystallographic texture data in a FAIR-er way, including guidelines for formatting pole figures and for storing and distributing the raw texture data, especially in preparation for machine learning or data informatics applications.

Proceedings Inclusion?

Planned:

Keywords

ICME, Machine Learning, Other

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