Accelerating Catalyst Design via AI: High-Throughput Screening and Machine Learning Reveal Defect-Enhanced Activity in Pt-Au Nanoclusters

Ilia Chepkasov

Submission Type: I want my submission to be considered for both oral and poster presentation.

Keywords: Pt-Au nanoclusters, CO oxidation, USPEX evolutionary algorithm, graphene defects, machine learning potentials, density functional theory, high-throughput screening, adsorption-induced reconstruction.

TL;DR: AI-driven evolutionary search and machine learning reveal that graphene defects activate gold atoms and trigger dynamic restructuring in Pt-Au nanoclusters, enabling highly efficient low-temperature CO oxidation.

Confirmation Of Submission Requirements: I submit an abstract. It uses the template provided on the submission page and is no longer than 2 pages.

Submission Number: 387