Please use this identifier to cite or link to this item: https://hdl.handle.net/1959.11/61390
Title: Low-Cost Pt Alloys for Heterogeneous Catalysis Predicted by Density Functional Theory and Active Learning
Contributor(s): Li, Xinyu (author); Chiong, Raymond  (author)orcid ; Hu, Zhongyi (author); Page, Alister J (author)
Publication Date: 2021-08
DOI: 10.1021/acs.jpclett.1c01851
Handle Link: https://hdl.handle.net/1959.11/61390
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-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, 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.

Publication Type: Journal Article
Source of Publication: Journal of Physical Chemistry Letters, 12(30), p. 7305-7311
Publisher: American Chemical Society
Place of Publication: United States of America
ISSN: 1948-7185
Fields of Research (FoR) 2020: 4602 Artificial intelligence
Peer Reviewed: Yes
HERDC Category Description: C1 Refereed Article in a Scholarly Journal
Appears in Collections:Journal Article
School of Science and Technology

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