Unsupervised pattern recognition on the surface of simulated metal nanoparticles for catalytic applications

Abstract

The application of supervised machine learning to the study of catalytic metal nanoparticles has been shown to deliver excellent performance for a range of predictive tasks. However, this success assumes that the particles have been thoroughly characterised and that the property labels are known. Even in exclusively computational studies, the labelling of metal nanoparticles remains the bottleneck for most machine learning studies due to either high computational costs or low relevance to the experimental properties of interest. To facilitate more widespread use of machine learning in catalysis, a computationally affordable strategy to describe metal nanoparticles by a label that is relevant to their catalytic activities is needed. In this study we propose an entirely data-driven approach that can be automated to characterise the patterns and catalytic activities of the surface atoms of simulated metal nanoparticles, and evaluate its utility for catalytic applications.