Development of High-Entropy Alloys with Optimized, Rare-Earth-Free Magnetocaloric Properties: AI-Driven Selection and Experimental Validation

• Grant number: 24/21293-0
• Support Opportunities: Scholarships in Brazil - Doctorate
• Start date: May 01, 2025
• End date: February 28, 2029
• Field of knowledge: Engineering - Materials and Metallurgical Engineering - Physical Metallurgy
• Principal Investigator: Francisco Gil Coury
• Grantee: Caroline Binde Stoco
• Host Institution: Centro de Ciências Exatas e de Tecnologia (CCET). Universidade Federal de São Carlos (UFSCAR). São Carlos , SP, Brazil
• Associated research grant: 22/02770-7 - Integrated Computational Materials Engineering ICME: applied to modeling, production, characterization and testing high entropy alloys, AP.PNGP.PI

Abstract

This project aims to develop and optimize high-entropy alloys (HEAs) with magnetocaloric effects (MCE) for application in sustainable magnetic refrigeration technologies. Considering that current refrigeration solutions based on gas compression cycles face environmental and efficiency challenges, the use of solid, emission-free magnetocaloric materials offers a promising pathway. To address the scarcity of magnetocaloric materials with optimized cyclic properties, this study will employ a genetic algorithm coupled with machine learning techniques and first-principles calculations, enabling the exploration of a vast compositional space of HEAs. The objective is to identify HEAs without the addition of rare earth elements that exhibit robust magnetocaloric effects, low thermal and magnetic hysteresis, and acceptable mechanical properties. To achieve this, alloys that demonstrate minimal volume change during the magnetostructural austenite-martensite transformation and a Curie temperature close to room temperature will be selected. Finally, the proposed methodology also includes experimental validation. Two alloys with optimized properties will be selected by the genetic algorithm, synthesized in an arc furnace using high-purity materials, and characterized through X-ray diffraction (XRD), scanning electron microscopy (SEM), differential scanning calorimetry (DSC), and magnetic measurements. The experiments aim to validate the predictions made by computational modeling.