Redefining Catalysis Predictions Through Physics-Based Gaussian Model and Data-Driven Benchmarks: AuPd Alloy in Oxygen Reduction Reaction Catalysis for Fuel Cell Applications

Neeru Chaudhary, Chuhong Lin, Ng Yan Ting, Kedar Hippalgaonkar, Tej Salil Choksi

Keywords: Density Functional Theory, Fuel Cells, Gaussian Process Regression, Machine Learning in Catalysis, OpenCatalyst (OC) Project, Oxygen Reduction Reaction, Nanoparticle Stability

TL;DR: A machine learning-guided DFT framework with Gaussian Process Regression (GPR) optimizes AuPd bimetallic catalysts for fuel cells. Integration with OpenCatalyst (OC) models enhances ORR performance prediction with high accuracy and efficiency.

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: 124