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Intelligible high-entropy materials: developing models, data and applications

Abstract

High-entropy alloys are materials formed by at least five metallic elements in proportions of similar composition for each metal. The random mixing of chemical elements increases the entropy of these materials, stabilizing phases with uniform and disordered solid solutions. For these materials, the variety of composition is immense, with combinatorial explosion with the increase of different chemical elements in the alloy. In exploring the possibilities of configurations of these materials, machine learning algorithms can be applied for classification, clustering and pattern recognition in material databases. New materials can be discovered in this space of high-entropy-alloy configurations, with physical and chemical properties specially designed for a given application, such as catalysis for the electrochemical production of hydrogen. In this project, we will investigate the properties of nanoparticles from high-entropy alloys through automated ab initio simulations and analyze this data with machine learning algorithms based on neural networks. We aim to create high entropy material databases for pattern recognition and categorization by machine learning algorithms. Finally, we aim to discover new materials that can be synthesized and applied as catalysts for the production of green hydrogen through the electrolysis of water. (AU)

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