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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) ; 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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