Please use this identifier to cite or link to this item:
https://hdl.handle.net/2440/137287
Citations | ||
Scopus | Web of Science® | Altmetric |
---|---|---|
?
|
?
|
Type: | Journal article |
Title: | Data-driven Machine Learning for Understanding Surface Structures of Heterogeneous Catalysts |
Author: | Li, H. Jiao, Y. Davey, K. Qiao, S. |
Citation: | Angewandte Chemie International Edition, 2023; 62(9):e202216383-1-e202216383-13 |
Publisher: | Wiley |
Issue Date: | 2023 |
ISSN: | 1433-7851 1521-3773 |
Statement of Responsibility: | Haobo Li, Yan Jiao, Kenneth Davey, and Shi-Zhang Qiao |
Abstract: | The design of heterogeneous catalysts is necessarily surface-focused, generally achieved via optimization of adsorption energy and microkinetic modelling. A prerequisite is to ensure the adsorption energy is physically meaningful is the stable existence of the conceived active-site structure on the surface. The development of improved understanding of the catalyst surface, however, is challenging practically because of the complex nature of dynamic surface formation and evolution under in-situ reactions. We propose therefore data-driven machine-learning (ML) approaches as a solution. In this Minireview we summarize recent progress in using machine-learning to search and predict (meta)stable structures, assist operando simulation under reaction conditions and micro-environments, and critically analyze experimental characterization data. We conclude that ML will become the new norm to lower costs associated with discovery and design of optimal heterogeneous catalysts. |
Keywords: | Heterogeneous Catalysts; Machine Learning (ML); Operando Computation; Surface Structures; In-Situ Characterization |
Description: | First published: 12 December 2022 |
Rights: | © 2022 The Authors. Angewandte Chemie International Edition published by Wiley-VCH GmbH. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. |
DOI: | 10.1002/anie.202216383 |
Grant ID: | http://purl.org/au-research/grants/arc/FL170100154, http://purl.org/au-research/grants/arc/DP220102596 http://purl.org/au-research/grants/arc/FT190100636 |
Published version: | http://dx.doi.org/10.1002/anie.202216383 |
Appears in Collections: | Chemical Engineering publications |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
hdl_137287.pdf | Published version | 2.97 MB | Adobe PDF | View/Open |
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.