Abstract:

Sustainable energy systems require efficient electrochemical water splitting, currently hindered by sluggish hydrogen and oxygen evolution reactions (HER/OER). This study presents a computational framework integrating density functional theory (DFT) and machine learning (ML) to accelerate the design of high-performance electrocatalysts. By leveraging high-throughput data, we establish property-structure relationships across single-atom catalysts, alloys, and high-entropy materials. We analyze critical thermodynamic descriptors, including the $d$-band center and adsorption free energies ($\Delta G_{H^}$ and $\Delta G_{OOH^}$), to elucidate electronic interactions and active site synergies. Ultimately, this integrated approach enables rapid screening, providing robust guidelines for developing cost-effective, highly active electrocatalysts for scalable green hydrogen production.

Speaker:

Juarez L. F. Da Silva is a professor and researcher at São Carlos Institute of Chemistry of the University of São Paulo (IQSC-USP). His research focuses on the Computational Materials Science and Chemistry fields, employing first-principles (ab initio) methods based on Density Functional Theory to study metal surfaces, molecular adsorption, oxides, semiconductors, clusters, nanoparticles and others, developing algorithms for the global structural optimization of nanoparticles. He is the leader of Quantum Theory of Nanomaterials (QTNano) research laboratory at IQSC-USP and the principal investigator of the Computational Materials Design division at Center for Innovation on New Energies (CINE).

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