Low-Cost Pt Alloys for Heterogeneous Catalysis Predicted by Density Functional Theory and Active Learning

Xinyu Li; Raymond Chiong; Zhongyi Hu; Alister J Page

doi:10.1021/acs.jpclett.1c01851

Low-Cost Pt Alloys for Heterogeneous Catalysis Predicted by Density Functional Theory and Active Learning

J Phys Chem Lett. 2021 Aug 5;12(30):7305-7311. doi: 10.1021/acs.jpclett.1c01851. Epub 2021 Jul 28.

Authors

Xinyu Li¹, Raymond Chiong¹, Zhongyi Hu², Alister J Page³

Affiliations

¹ School of Electrical Engineering and Computing, The University of Newcastle, Callaghan, New South Wales 2308, Australia.
² School of Information Management, Wuhan University, Wuhan 430072, China.
³ Discipline of Chemistry, School of Environmental and Life Sciences, The University of Newcastle, Callaghan, New South Wales 2308, Australia.

PMID: 34319099
DOI: 10.1021/acs.jpclett.1c01851

Abstract

Pt is a key high-performing catalyst for important chemical conversions, such as biomass conversion and water splitting. Limited Pt reserves, however, demand that we identify more sustainable alternative catalyst materials for these processes. Here, we combine state-of-the-art graph neural networks and crystal graph machine learning representations with active learning to discover new, low-cost Pt alloy catalysts for biomass reforming and hydrogen evolution reactions. We identify 12 Pt-based alloys which have comparable catalytic activity to that of the exemplar Pt(111) surface. Notably, Cu₃Pt and FeCuPt₂ exhibit near identical catalytic performance as that of Pt(111). These results demonstrate the potential of machine learning for predicting new catalytic materials without recourse to expensive DFT geometry optimizations, the current bottleneck impeding high-throughput materials discovery. We also examine the performance of d-band theory in elucidating trends in binary and ternary Pt alloys.