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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

Prediction of Metal-Insulator-Transition (MIT) Compounds from Density of States (DOS) Data Using Image-Based Deep Learning

Author(s)

Aleyna Daldal, Irmak Sargın

On-Site Speaker (Planned)

Aleyna Daldal

Abstract Scope

Metal-insulator transition (MIT) compounds can change their conductivity states with external factors which give them great potential for use in the design of next-generation electronic devices; however, identifying MIT compounds remains experimentally and computationally costly. In this study, we demonstrated that convolutional neural networks (CNNs) trained on DFT-based density of state (DOS) images can classify materials as metals, insulators, or MIT with 90% accuracy, potentially bypassing traditional quantum-mechanical calculations. By leveraging Shapley Additive exPlanations (SHAP) analysis, we revealed the dominant role of d-orbitals, uncovering the key orbital features. Compared with DFT screening, our approach reduces computational costs by orders of magnitude, enabling the rapid discovery of MIT candidates. This study bridges materials informatics with condensed-matter physics, offering a scalable framework for property prediction from electronic structure data.

Proceedings Inclusion?

Planned:

Keywords

Computational Materials Science & Engineering, ICME, Machine Learning

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