基于机器学习高通量筛选二氧化碳还原电催化剂的研究进展

doi:10.11896/cldb.23110048

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Abstract In the context of continuous growth in global energy demand, limited fossil fuel resources, and the increasingly serious impacts of carbon dio-xide emissions on the climate, urgent reductions in carbon dioxide emissions are required. The electrochemical reduction of carbon dioxide (CO₂ RR) based on clean electricity is an ideal way to alleviate fossil fuel consumption and greenhouse gas emissions. Traditional catalyst research and development models mainly rely on experimental trial-and-error methods, which are time consuming and limited in their ability to meet the needs for efficient catalysts. The rapid development of data science technologies, such as machine learning, has brought paradigm changing opportunities for catalyst research and development. High-throughput computing combined with machine learning has become an important approach in the design of electrocatalysts in recent years. Thus, this paper reviews contemporary research on high-throughput computing combined with machine learning to guide catalyst development, including the principles of catalyst design, simulation calculation strategies, and the construction of machine learning models. The combination of high-throughput computing and machine learning should provide a new method for efficient screening and development of CO₂ RR catalysts, which should broad the application of artificial intelligence in catalyst screening design.

Key words： CO₂ RR density functional theory(DFT) machine learning descriptor catalyst screening

Published: 10 January 2025 Online: 2025-01-10

CLC number:

TQ151

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	Articles by authors
	LI Ge
	MA Ziran
	LYU Fei
	PENG Shengpan
	TONG Zhenwei

Cite this article:

LI Ge,MA Ziran,LYU Fei, et al. Advances in the Application of Machine Learning in Electrocatalytic Carbon Dioxide Reduction[J]. Materials Reports, 2025, 39(1): 23110048-13.

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https://www.mater-rep.com/EN/10.11896/cldb.23110048 OR https://www.mater-rep.com/EN/Y2025/V39/I1/23110048

1 Davis S J, Caldeira K, Matthews H D. Science, 2010, 329(5997), 1330.
2 Evans G W. Annual Review of Psychology, 2019, 70(1), 449.
3 UN Environment Programme. Emissions gap report 2023:broken Record. 2023.
4 Zhao X L, Xiao J Y, Hou J M, et al. Economic and scale prediction of CO₂ capture, utilization and storage technologies in China, China's carbon neutrality road, China Electric Power Press, Beijing, 2021, pp.153.
5 Tukendra K, Satya E J. Energy & Fuels, 2023, 37(5), 3570.
6 Michele A, Angela D, Antonella A. Chemical Reviews, 2014, 114(3), 1709.
7 Artz J, Müller T E, Thenert K, et al. Chemical Reviews, 2018, 118(2), 434.
8 Xue Y, Zhou X X, Zhu Y F, et al. Energy & Fuels, 2021, 35(7), 5696.
9 Roy S C, Varghese O K, Paulose M, et al. ACS Nano, 2010, 4(3), 1259.
10 Kaliyaperumal A, Gupta P, Prasad Y S S, et al. ACS Engineering Au, 2023, 3 (6), 403.
11 Choi C H, Chung K, Nguyen T T H, et al. ACS Energy Letters, 2018, 3(6), 1415.
12 Jafarzadeh M, Daasbjerg K. ACS Applied Energy Materials, 2023, 6(13), 6851.
13 Fiedler L, Shah K, Bussmann M, et al. Physical Review Materials, 2022, 6(4), 040301.
14 Zheng R, Xie Z, He M, et al. Joule, 2022, 6, 1148.
15 Chen L, Zhang X, Chen A, et al. Chinese Journal of Catalysis, 2022, 43(1), 11.
16 Mai H X, Le T C, Chen D H, et al. Chemical Reviews, 2022, 122, 13478.
17 Salkind A J, Fennie C, Singh P, et al. Journal of Power Sources, 1999, 80(1-2), 293.
18 Morgan D, Ceder G, Curtarolo S. MRS Proceedings, 2003, 804, 305.
19 Curtarolo S, Morgan D, Persson K, et al. Physical Review Letters, 2003, 91(13), 135503.
20 Morgan D, Ceder G, Curtarolo S. Measurement Science and Technology, 2005, 16(1), 296.
21 Feldman K, Agnew S R. JOM, 2014, 66(3), 336.
22 Widener A. Chemical & Engineering News Archive, 2013, 91(31), 25.
23 Li H, Jiang Y, Li X, et al. Journal of the American Chemical Society, 2023, 145(26), 14335.
24 Yohannes A G, Lee C, Talebi P, et al. ACS Catalysis, 2023, 13(13), 9007.
25 Wei J, Yang Y, Liu J, et al. Journal of CO₂Utilization 2022, 64, 102165.
26 Sun Z H, Yin H, Liu K L, et al. Smart Materials 2022, 3(1), 68.
27 Wan X, Zhang Z, Niu H, et al. Journal of Physical Chemistry Letters, 2021, 12(26), 6111.
28 Feng H, Ding H, He P, et al. Journal of Materials Chemistry A, 2022, 10(36), 18803.
29 Timoshenko J, Jeon H S, Sinev I, et al. Chemical Science, 2020. 11(14), 3727.
30 Guo Y, He X R, Su Y M, et al. Journal of the American Chemical Society, 2021, 143, 5755.
31 Song Z, Zhou Q, Lu S, et al. Journal of Physical Chemistry Letters, 2023, 14(14), 3594.
32 Zhong M, Tran K, Min Y, et al. Nature, 2020, 581(7807), 178.
33 Tran K, Ulissi Z W. Nature Catalysis, 2018, 1(9), 696.
34 Roy D, Mandal S C, Pathak B. ACS Applied Material Interfaces, 2021, 13(47), 56151.
35 Pedersen J K, Batchelor T A A, Bagger A, et al. ACS Catalysis, 2020, 10(3), 2169.
36 Ma X, Li Z, Achenie L E K, et al. Journal of Physical Chemistry Letters, 2015, 6, 3528.