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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

Data Informatics for Featurization and Discovery of Metal to Insulator Transition Materials

Author(s)

Gulcehre Duygu Yuksel, Irmak Sargin

On-Site Speaker (Planned)

Gulcehre Duygu Yuksel

Abstract Scope

Metal–insulator transition (MIT) materials can switch between conducting and insulating states offering high potential for future electronics and correlated systems. However, discovering new MIT compounds with desired properties remains difficult due to the vast space of possible compositions. This study utilizes machine learning approaches based on compositional data collected from literature. Supervised classification and unsupervised principal component analysis (PCA) were used to explore patterns among known metals, insulators, and MIT compounds. Physically meaningful features such as ionic radius deviation, global instability index, hybridization energy, and fractional ionicity defined the feature space. In both approaches, several previously unrecognized non-MIT materials, such as Magnéli phases and RNiO₃ series members, clustered near the MIT group, suggesting potential transition behavior. Additionally, new candidate compositions were proposed based on their similarity to known MITs. This study highlights the potential of interpretable, feature-driven ML tools in transition discovery, correlating experimental studies with computational MIT prediction.

Proceedings Inclusion?

Planned:

Keywords

Machine Learning, Electronic Materials, Computational Materials Science & Engineering

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