Clustering and Predictive Analysis of Magnetocaloric Materials for Sustainable Cooling Technologies

Magnetic refrigeration based on the magnetocaloric effect has emerged as a promising solid-state alternative to conventional vapor-compression cooling. The technology can potentially reduce dependence on conventional gaseous refrigerants and offers opportunities for compact, efficient and environmentally responsible cooling systems. However, the development of suitable magnetocaloric materials remains challenging because their performance depends simultaneously on magnetic, thermal, structural, chemical and compositional characteristics. Conventional trial-and-error approaches are therefore often time-consuming and resource intensive. This paper proposes an integrated data-driven framework combining clustering and predictive analysis for the systematic assessment of magnetocaloric materials. In the proposed approach, materials are represented through descriptors such as Curie temperature, magnetic entropy change, adiabatic temperature change, magnetization, magnetic hysteresis, thermal conductivity, density, composition, structural parameters and estimated environmental indicators. Unsupervised clustering can be employed to identify groups of materials with similar functional characteristics, while supervised machine-learning models can predict important magnetocaloric properties. The resulting framework can be used to classify materials into high-performance, application-specific and development-priority groups. The paper further discusses how data-driven screening can support the selection of materials for sustainable cooling by simultaneously considering magnetic performance, thermal compatibility, stability, cost, resource availability and environmental safety. Rather than replacing experimental validation, the proposed methodology is intended to reduce the search space and prioritize promising candidates for laboratory investigation. The study presents a conceptual pathway toward intelligent magnetocaloric material discovery and sustainable refrigeration system development.