Machine learning is transforming the study of heavy rare earth–based low-temperature magnetocaloric materials by enabling intelligent analysis of complex magnetic and thermal behaviors ❄️🧲🤖. These materials are crucial for next-generation solid-state refrigeration, and ML helps uncover hidden patterns within large experimental datasets that traditional methods often miss 📊🔍.
Advanced classification algorithms such as neural networks, support vector machines, and ensemble models efficiently categorize materials based on entropy change, magnetic transition, and crystal structure 🧠⚙️. By learning from multidimensional features, ML improves accuracy in predicting performance and stability under low-temperature conditions 🚀🔬.
This data-driven approach accelerates material discovery, reduces experimental cost, and supports sustainable cooling technologies 🌱✨. Integrating machine learning with magnetocaloric research opens new pathways for designing energy-efficient refrigeration systems and advancing smart materials science in the era of AI 🌍🤖❄️.
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