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Classification of Platinum Nanoparticle Catalysts using Machine Learning
preprintsubmitted 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.
Email Address of Submitting Authoramanda.email@example.com
InstitutionAustralian National University
ORCID For Submitting Author0000-0002-4784-2382
Declaration of Conflict of InterestNo conflicts of interest to declare.
Read the published paper
in Journal of Applied Physics