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Classification of Platinum Nanoparticle Catalysts using Machine Learning

preprint
submitted on 21.05.2020, 02:48 and posted on 21.05.2020, 13:04 by Amanda J. Parker, George Opletal, Amanda Barnard
Computer simulations and machine learning provide complementary ways of identifying structure/property relationships that are typically targeting toward predicting the ideal singular structure to maximise the performance on 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 (ORR), hydrogen oxidation (HOR) and hydrogen evolution (HER) reactions. By including classification prior to regression we identified two distinct classes of nanoparticles, and subsequently generate 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.

History

Email Address of Submitting Author

amanda.s.barnard@anu.edu.au

Institution

Australian National University

Country

Australia

ORCID For Submitting Author

0000-0002-4784-2382

Declaration of Conflict of Interest

No conflicts of interest to declare.