High Entropy Materials as Catalysts for the Production of Green Hydrogen: A Computational Approach Guided by Data and Artificial Intelligence

• Grant number: 25/13975-7
• Support Opportunities: Scholarships in Brazil - Doctorate (Direct)
• Start date: January 01, 2026
• End date: January 31, 2030
• Field of knowledge: Physical Sciences and Mathematics - Physics - Condensed Matter Physics
• Principal Investigator: Pedro Alves da Silva Autreto
• Grantee: Guilherme Gurian Dariani
• Host Institution: Centro de Ciências Naturais e Humanas (CCNH). Universidade Federal do ABC (UFABC). Santo André , SP, Brazil
• Associated research grant: 23/09820-2 - Materials by design: from quantum materials to energy applications, AP.TEM

Abstract

The production of green hydrogen through water electrolysis is a promising alternative for sustainable energy storage and use, but it critically depends on efficient and low-cost catalysts. In this context, high entropy materials (HEMs) have emerged as potential candidates due to their structural stability, compositional diversity, and unique electrocatalytic properties. However, the vast compositional space of HEMs makes it difficult to explore them solely through experimental or conventional computational methods. This project proposes to integrate simulations based on Density Functional Theory (DFT), machine learning (ML), and Natural Language Processing (NLP) techniques to accelerate the discovery and rational design of HEMs as catalysts for the Hydrogen Evolution Reaction (HER). Predictive models will be developed to estimate key properties-such as the free energy of hydrogen adsorption-by combining computational data and experimental information automatically extracted from the scientific literature. This approach aims to identify more efficient catalyst candidates, reduce the computational cost of the search process, and contribute open-access data and tools to the scientific community. (AU)