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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-driven selection of lightweight manganite-based magnetocaloric materials for ambient temperature magnetic refrigeration

Author(s)

Tanjore V. Jayaraman

On-Site Speaker (Planned)

Tanjore V. Jayaraman

Abstract Scope

Magnetic refrigeration (MR) is a promising alternative to traditional cooling technologies, offering high energy efficiency and reduced environmental impact. Within this domain, perovskite manganites of the form R₁₋ₓTₓMnO₃ (R = rare-earth, T = transition metal) stand out as tunable magnetocaloric materials (MCMs) due to their adjustable Curie temperatures (100–350 K), low density, and fabrication versatility. This work applies a data-driven, informatics-based framework to systematically evaluate and rank hundreds of R₁₋ₓTₓMnO₃ compositions using multiple-attribute decision-making (MADM) and advanced statistical methods. Key magnetocaloric metrics—magnetic entropy change, refrigerant capacity, and Curie temperature—serve as evaluation criteria. MADM techniques, including TOPSIS, GRA, etc., were employed to generate robust and consistent performance rankings. Complementary statistical clustering validated the findings and revealed performance trends among composition families. This approach demonstrates the power of data informatics in guiding materials discovery and design, accelerating the identification of high-performance MCMs for sustainable MR technologies across multiple scales.

Proceedings Inclusion?

Planned:

Keywords

Magnetic Materials, Energy Conversion and Storage, Computational Materials Science & Engineering

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