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

材料导报

2025, Vol. 39

Issue (1): 23110048-13 https://doi.org/10.11896/cldb.23110048

无机非金属及其复合材料

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

李歌^*, 马子然, 闾菲, 彭胜攀, 佟振伟

北京低碳清洁能源研究院, 北京 102211

Advances in the Application of Machine Learning in Electrocatalytic Carbon Dioxide Reduction

LI Ge^*, MA Ziran, LYU Fei, PENG Shengpan, TONG Zhenwei

National Institute of Clean-and-Low-Carbon Energy, Beijing 102211, China

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摘要随着全球能源需求不断增长,化石燃料资源有限和二氧化碳排放对气候变化的影响愈加严重,减少二氧化碳排放已迫在眉睫。基于绿电的电化学还原二氧化碳(CO₂ RR)方法是缓解化石燃料消耗和温室气体排放的理想途径。传统催化剂的研发模式主要依赖实验试错方法,难以满足对高效催化剂的研发需求。快速发展的机器学习等数据科学技术为催化剂研发带来范式变革的契机。高通量计算结合机器学习已经成为近年来电催化剂配方设计中的重要手段之一。基于此,本文概述了近年来高通量计算结合机器学习指导催化剂开发的研究成果,包括催化剂设计的原理、模拟计算的策略以及机器学习模型的构建。通过将高通量计算和机器学习结合,可以加速催化剂设计过程,为CO₂ RR催化剂的高效筛选和开发提供了新方法,拓宽人工智能在催化剂筛选设计中的应用。

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关键词: CO₂ RR 密度泛函理论计算机器学习描述符催化剂筛选

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

出版日期: 2025-01-10 发布日期: 2025-01-10

ZTFLH:

TQ151

基金资助: 国家能源集团科技创新项目(ST930022006C)

通讯作者: *李歌，博士，北京低碳清洁能源研究院CCUS中心教授级高级工程师。先后从事CO₂ 矿化、废水处理、粉煤灰综合利用、NO_x 催化还原、WOC_s 催化氧化、绿氨合成、CO₂ 电催化等能源化工与环境领域的研发工作。目前主要研究领域为能源化工与环境催化关键材料的研发及机理研究。ge.li.f@chnenergy.com.cn

引用本文:

李歌, 马子然, 闾菲, 彭胜攀, 佟振伟. 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[J]. 材料导报, 2025, 39(1): 23110048-13.
LI Ge, MA Ziran, LYU Fei, PENG Shengpan, TONG Zhenwei. Advances in the Application of Machine Learning in Electrocatalytic Carbon Dioxide Reduction. Materials Reports, 2025, 39(1): 23110048-13.

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https://www.mater-rep.com/CN/10.11896/cldb.23110048 或 https://www.mater-rep.com/CN/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.

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