Classification of platinum nanoparticle catalysts using machine learning

Abstract

Computer simulations and machine learning provide complementary ways of identifying structure/property relationships that are typically targeting toward predicting the ideal singular structure to maximize the performance in a given application. This can be inconsistent with experimental observations that measure the collective properties of entire samples of structures that contain distributions or mixture of structures, even when synthesized and processed with care. Metallic nanoparticle catalysts are an important example. In this study, we have used a multi-stage machine learning workflow to identify the correct structure/property relationships of Pt nanoparticles relevant to oxygen reduction, hydrogen oxidation, and hydrogen evolution reactions. By including classification prior to regression, we identified two distinct classes of nanoparticles and subsequently generated the class-specific models based on experimentally relevant criteria that are consistent with observations. These multi-structure/multi-property relationships, predicting properties averaged over a large sample of structures, provide a more accessible way to transfer data-driven predictions into the lab.