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

Author(s)
Li, Xinyu
Chiong, Raymond
Hu, Zhongyi
Page, Alister J
Publication Date
2021-08
Abstract
<p>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-ofthe-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, Cu<sub>3</sub>Pt and FeCuPt<sub>2</sub> 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 <i>d</i>-band theory in elucidating trends in binary and ternary Pt alloys.</p>
Citation
Journal of Physical Chemistry Letters, 12(30), p. 7305-7311
ISSN
1948-7185
Link
Publisher
American Chemical Society
Title
Low-Cost Pt Alloys for Heterogeneous Catalysis Predicted by Density Functional Theory and Active Learning
Type of document
Journal Article
Entity Type
Publication

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