机器学习加速的二氧化碳全局反应网络动力学模拟

doi:10.1016/S1872-2067(26)65208-4

催化学报 ›› 2026, Vol. 91: 163-175.DOI: 10.1016/S1872-2067(26)65208-4

机器学习加速的二氧化碳全局反应网络动力学模拟

梁小立^a^,¹, 王兆余^a^,¹, 颜世林^a, 尹婷婷^a, 张炜^b^,^c^,^*(), 张浩^a^,^*(), 刘小红^d, 赵刘斌^a^,^*()

^a 西南大学化学化工学院, 重庆 400715
^b 中国科学院重庆绿色智能技术研究院, 重庆 400714
^c 中南林业科技大学, 湖南长沙 410004
^d 新加坡国立大学重庆研究院, 重庆 401123

收稿日期:2026-03-19 接受日期:2026-05-13 出版日期:2026-12-18 发布日期:2026-10-13
通讯作者: ^*电子信箱: zhangwei@csuft.edu.cn (张炜),
haozhang@swu.edu.cn (张浩),
lbzhao@swu.edu.cn (赵刘斌).
作者简介:¹共同第一作者.
基金资助:
表界面化学全国重点实验室开放课题(202421);重庆市自然科学基金(CSTB2024NSCQ-MSX0344);重庆市自然科学基金(CSTB2023NSCQ-MSX0768);重庆市自然科学基金(cstc2021jcyj-jqX0031);国家自然科学基金(51832008);国家自然科学基金(62175237);中国科学院“西部之光”交叉学科团队项目(xbzg-zdsys-202106);中国科学院区域发展青年学者计划(2021)

Machine learning-accelerated electrocatalytic microkinetic model for global reaction network simulation of CO₂ reduction reaction

Xiao-Li Liang^a^,¹, Zhao-Yu Wang^a^,¹, Shi-Lin Yan^a, Ting-Ting Yin^a, Wei Zhang^b^,^c^,^*(), Hao Zhang^a^,^*(), Xiaohong Liu^d, Liu-Bin Zhao^a^,^*()

^a Department of Chemistry, School of Chemistry and Chemical Engineering, Southwest University, Chongqing 400715, China
^b Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing 400714, China
^c State Key Laboratory of Woody Oil Resource Utilization, College of Materials and Energy, Central South University of Forestry and Technology, Changsha 410004, Hunan, China
^d National University of Singapore (Chongqing) Research Institute, Chongqing 401123, China

Received:2026-03-19 Accepted:2026-05-13 Online:2026-12-18 Published:2026-10-13
About author:¹Contributed equally to this work.
Supported by:
Open Funds of State Key Laboratory of Physical Chemistry of Solid Surfaces(Xiamen University No.202421);Natural Science Foundation of Chongqing, China(CSTB2024NSCQ-MSX0344);Natural Science Foundation of Chongqing, China(CSTB2023NSCQ-MSX0768);Natural Science Foundation of Chongqing, China(cstc2021jcyj-jqX0031);Na-tional Natural Science Foundation of China(51832008);Na-tional Natural Science Foundation of China(62175237);Interdisciplinary Team Project under Auspices of “Light of West” Pro-gram in Chinese Academy of Sciences(xbzg-zdsys-202106);Young Scholar of Regional Development Program in Chinese Academy of Sciences(2021)

摘要/Abstract

摘要：

电化学二氧化碳还原反应(CO₂RR)为实现碳资源循环利用和可持续能源存储提供了重要途径. 铜基催化剂因其独特的电子结构, 能够将CO₂转化为高附加值烃类及含氧化合物, 因而受到广泛关注. 理论计算可在原子尺度上指导实验研究, 通过对反应空间内所有可能的中间体及反应路径进行严格的热力学及动力学计算, 为筛选高效催化剂材料提供理论支撑. 密度泛函理论(DFT)是该领域最主要的理论研究工具, 然而CO₂RR涉及多种表面中间体和复杂反应网络, 且表面物种的吸附结构会随催化剂材料组成和形貌的改变愈加复杂多样, 可能的吸附构型数量急剧增加, 直接使用DFT进行全局第一性原理计算模拟时, 计算消耗随中间体数目增多及反应路径的扩展而急剧增加, 难以满足大规模快速材料筛选的需求.
近年来, 基于图神经网络的机器学习模型可直接从原子结构出发快速预测吸附构型与能量, 计算速度较DFT提升数个数量级, 但直接机器学习弛豫的能量误差仍远超可靠动力学预测所需的精度, 精度与效率之间的权衡是该领域的核心挑战. 针对以上问题, 本文将机器学习加速计算、全局反应网络模拟和电催化微动力学模型三种方法相结合, 构建了系统的理论研究框架. 其中, 机器学习AdsorbML-FT工作流通过少量的单点结构数据对预训练模型进行特异性微调, 在保持力收敛精度为0.02 eV/Å的同时将DFT优化步骤加速24倍, 并可自动识别最优吸附位点; 全局反应网络模拟穷举了所有可能的反应路径, 突破了传统最优路径方法仅考虑单一能量最优通道的局限; 基于Marcus-Gerischer电荷转移理论和稳态近似的微动力学模型, 实现了电位依赖的催化活性与产物选择性的定量预测. 基于该框架, 本文系统解析了Cu(111)表面CO₂生成C₁产物的反应机理, 并系统筛选了37种Cu₃M₁合金催化剂, 最终发现Cu₃Al₁为最优催化剂, 在较低过电位下兼具高催化活性和高CH₄选择性, 架起了理论模拟与实验测量之间的桥梁.
综上, 本文将机器学习加速算法、全局反应网络模拟和微动力学模型有机结合, 建立了从机理认知到性能预测的理论框架, 筛选出Cu₃Al₁为高效CO₂甲烷化催化剂. 同时该框架可拓展至C₂产物体系, 为电催化剂的理性设计提供了新的理论工具.

关键词: CO₂还原反应, 机器学习, 密度泛函理论, 微观动力学模型, 反应机理

Abstract:

Electrochemical CO₂ reduction represents a sustainable route for energy storage and carbon utilization. Despite copper-based catalysts uniquely enabling hydrocarbons and oxygenates product formation, advancing this technology requires deeper mechanistic insights and accelerated catalyst screening. Herein, we establish a novel electrocatalysis research framework integrating three synergistic components: (1) With a force convergence accuracy of 0.02 eV/Å, the AdsorbML-FT workflow can achieve a 24-fold acceleration in DFT optimization steps whilst accounting for adsorption site screening, thereby enabling more efficient energy calculations for general substrate-adsorbate systems; (2) the global reaction network simulation of CO₂RR enabling broader coverage of reaction mechanism including 22 surface species and 40 elementary reactions beyond traditional optimal-path limitations; (3) the electrocatalytic microkinetic model ensuring greater accuracy in predicting both catalytic activity and product selectivity. This integrated approach quantitatively decodes the CO₂-to-C₁ mechanism on Cu(111) and systematically screens 37 Cu₃M₁ alloys. We identify Cu₃Al₁ as an optimal catalyst, driving CO conversion at relatively low overpotentials with simultaneously high catalytic activity and CH₄ product selectivity. The proposed machine learning-accelerated electrocatalytic microkinetic model bridges theoretical simulations with experimental measurements, offering a transformative tool for rapid electrocatalyst discovery.

Key words: CO₂ reduction reaction, Machine learning, Density functional theory, Microkinetic model, Reaction mechanism

梁小立, 王兆余, 颜世林, 尹婷婷, 张炜, 张浩, 刘小红, 赵刘斌. 机器学习加速的二氧化碳全局反应网络动力学模拟[J]. 催化学报, 2026, 91: 163-175.

Xiao-Li Liang, Zhao-Yu Wang, Shi-Lin Yan, Ting-Ting Yin, Wei Zhang, Hao Zhang, Xiaohong Liu, Liu-Bin Zhao. Machine learning-accelerated electrocatalytic microkinetic model for global reaction network simulation of CO₂ reduction reaction[J]. Chinese Journal of Catalysis, 2026, 91: 163-175.

×
扫码分享

导出引用管理器 EndNote|Ris|BibTeX

链接本文: https://www.cjcatal.com/CN/10.1016/S1872-2067(26)65208-4

https://www.cjcatal.com/CN/Y2026/V91/I12/163

图/表 6

Fig. 1. Workflow of ML accelerated DFT calculations by AdsorbML-FT: (1) Initial configuration sampling: initial “adsorbate + substrate” configurations are generated for different combinations of adsorbate and substrate. (2) ML pre-screening: all initial configurations undergo ML-driven relaxation yielding ML-pre-relaxed configurations. (3) 3-Step DFT calculation: A three-step consecutive relaxation cycle is performed on the pre-relaxed configurations to calculate DFT single-point (DFT(SP)), which are used for validation or subsequent fine-tuning. (4) Fine tuning training: Use DFT single-point data for fine-tuning the pre-trained model. (5) Refined ML prediction: The fine-tuned ML (FT) model reprocesses different combinations of adsorbate and substrate to obtain a new set of more accurate ML-pre-relaxed optimal configurations. (6) Final DFT relaxation: The optimal configuration determined by ML(FT) will undergo a full DFT relaxation.

Fig. 2. (a) Top view of the Cu3Al1(111) surface. (b) DFT relaxation and adsorption energy changes of initial structures obtained by three methods (Heuristic, ML, ML(FT)), where the “SCF step” corresponds to the self-consistent field (SCF) calculation step in DFT. (c) Adsorption potential energy surface of CO on the surface shown in (a). (d) Adsorption energy profile along the x=5.3 Å line in (c), predicted by three different models，where ML(FT)-15 denotes a machine learning model trained using 15 DFT data points, and ML(FT)-503 denotes a machine learning model trained using 503 DFT data points.

Fig. 3. (a) Comparison of adsorption energies between pre-trained ML predictions and DFT(SP) on the validation set. (b) Validation set performance: Fine-tuned ML model (537 training points) vs. DFT(SP) adsorption energies. (c) Reproducibility test: Violin plot showing the distribution of R2 scores for adsorption energies on independently constructed DFT validation (three-step single-point calculations per adsorbate + substrate combination) points over 50 independent runs. (d) Assessment of initial structure quality: Absolute deviation (ΔEads) between DFT single-point energies of ML-predicted initial structures and final relaxed states across 48 systems.

Fig. 4. (a) Global reaction networks of CO2 reduction to C1 products. (b) Partial and total reaction rates as a function of electrode potential evaluated by GRN. (c) Faradaic efficiency as a function of electrode potential calculated by GRN. (d) and (e) Comparison of catalytic activities and product selectivities estimated by GRN and ORP.

Fig. 5. Total reaction rates (a) and activation barriers (b) of CORR on Cu3M1 catalysts at U=-1.0 V vs. SHE. (c,d) Proportional function fitting of total reaction rate with rate constant of rate-determining step at U=-0.3 and -1.0 V vs. SHE. (e) Calculated coefficients of determination as a function of electrode potential.

Fig. 6. (a,b) Product selectivity on different substrates with U=-0.3 and -1.0 V vs. SHE. Faradaic efficiency of CORR as a function of electrode potential on Cu3Au1 (c), Cu3In1 (d), and Cu3Al1 (e).

参考文献

[1]	J. Ye, N. Dimitratos, L. M. Rossi, N. Thonemann, A. M. Beale, R. Wojcieszak, Science, 2025, 387, eadn9388.
[2]	J. Han, X. Bai, X. Xu, X. Bai, A. Husile, S. Zhang, L. Qi, J. Guan, Chem. Sci., 2024, 15, 7870-7907.
[3]	J. Wang, Q. L. Gao, L. Chao, X. Gao, J. Gong, F. Song, J. Zhang, Y. Han, Q. Zhang, Chin. J. Catal., 2026, 85, 356-370.
[4]	H. Zhang, L. Jin, T. Zheng, X. Qiu, Y. Liu, D. Chen, Q. Xu, Y. Li, J. Lu, Chin. J. Catal., 2026, 82, 105-114.
[5]	Q. Bie, H. Yin, Y. Wang, H. Su, Y. Peng, J. Li, Chin. J. Catal., 2024, 57, 96-104.
[6]	J. Masa, C. Andronescu, W. Schuhmann, Angew. Chem. Int. Ed., 2020, 59, 15298-15312.
[7]	G. Leonzio, A. Hankin, N. Shah, Chem. Eng. Res. Des., 2024, 208, 934-955.
[8]	M. Jouny, W. Luc, F. Jiao, Ind. Eng. Chem. Res., 2018, 57, 2165-2177.
[9]	Y. Y. Birdja, E. Pérez-Gallent, M. C. Figueiredo, A. J. Göttle, F. Calle-Vallejo, M. T. M. Koper, Nat. Energy, 2019, 4, 732-745.
[10]	J. Yang, X. Wang, Y. Qu, X. Wang, H. Huo, Q. Fan, J. Wang, L.-M. Yang, Y. Wu, Adv. Energy Mater., 2020, 10, 2001709.
[11]	L. Xie, X. Liu, F. Huang, J. Liang, J. Liu, T. Wang, L. Yang, R. Cao, Q. Li, Chin. J. Catal., 2022, 43, 1680-1686.
[12]	J. Wang, H.-Y. Tan, Y. Zhu, H. Chu, H.-M. Chen, Angew. Chem. Int. Ed., 2021, 60, 17254-17267.
[13]	H. Xiao, T. Cheng, W. A. Goddard III, R. Sundararaman, J. Am. Chem. Soc., 2016, 138, 483-486.
[14]	H. Xiao, T. Cheng, W. A. Goddard III, J. Am. Chem. Soc., 2017, 139, 130-136.
[15]	C. Du, J. P. Mills, A. G. Yohannes, W. Wei, L. Wang, S. Lu, J.-X. Lian, M. Wang, T. Guo, X. Wang, H. Zhou, C.-J. Sun, J. Z. Wen, B. Kendall, M. Couillard, H. Guo, Z. Tan, S. Siahrostami, Y. A. Wu, Nat. Commun., 2023, 14, 6142.
[16]	M. Zheng, J. Zhang, P. Wang, H. Jin, Y. Zheng, S.-Z. Qiao, Adv. Mater., 2024, 36, 2307913.
[17]	B. Rhimi, M. Zhou, Z. Yan, X. Cai, Z. Jiang, Nano-Micro Lett., 2024, 16, 64.
[18]	S. Nitopi, E. Bertheussen, S. B. Scott, X. Liu, A. K. Engstfeld, S. Horch, B. Seger, I. E. L. Stephens, K. Chan, C. Hahn, J. K. Nørskov, T. F. Jaramillo, I. Chorkendorff, Chem. Rev., 2019, 119, 7610-7672.
[19]	Y. Hori, R. Takahashi, Y. Yoshinami, A. Murata, J. Phys. Chem. B, 1997, 101, 7075-7081.
[20]	A. Bagger, W. Ju, A. S. Varela, P. Strasser, J. Rossmeisl, ChemPhysChem, 2017, 18, 3266-3273.
[21]	Y. Hori, in: Modern Aspects of Electrochemistry, C. G. Vayenas, R. E. White, M. E. Gamboa-Aldeco eds., New York, NY: Springer New York, 2008, 89-189.
[22]	O. Zoubir, L. Atourki, H. Ait Ahsaine, A. BaQais, RSC Adv., 2022, 12, 30056-30075.
[23]	V. S. S. Mosali, A. M. Bond, J. Zhang, Nanoscale, 2022, 14, 15560-15585.
[24]	Y. Nakaya, S. Furukawa, Chem. Rev., 2023, 123, 5859-5947.
[25]	C. Ling, Y. Zhang, Q. Li, X. Bai, L. Shi, J. Wang, J. Am. Chem. Soc., 2019, 141, 18264-18270.
[26]	W. Kohn, L. J. Sham, Phys. Rev., 1965, 140, A1133-A1138.
[27]	P. Hohenberg, W. Kohn, Phys. Rev., 1964, 136, B864-B871.
[28]	Z. Chen, H. Wang, N. Q. Su, S. Duan, T. Shen, X. Xu, ACS Catal., 2018, 8, 5816-5826.
[29]	Y. Zang, Y. Liu, R. Lu, Q. Yang, B. Wang, M. Zhang, Y. Mao, Z. Wang, Y. Lum, Adv. Mater., 2025, 37, 2417034.
[30]	B. Hammer, J. K. Nørskov, Adv. Catal., 2000, 45, 71-129.
[31]	B. Hammer, Y. Morikawa, J. K. Nørskov, Phys. Rev. Lett., 1996, 76, 2141-2144.
[32]	X. Chen, R. Lu, C. Li, W. Luo, R. Yu, J. Zhu, L. Lv, Y. Dai, S. Gong, Y. Zhou, W. Xiong, J. Wu, H. Cai, X. Wu, Z. Deng, B. Xing, L. Su, F. Wang, F. Chao, W. Chen, C. Xia, Z. Wang, L. Mai, Nat. Commun., 2025, 16, 1927.
[33]	J.-H. Liu, L.-M. Yang, E. Ganz, RSC Adv., 2019, 9, 27710-27719.
[34]	J.-H. Liu, L.-M. Yang, E. Ganz, Energy Environ. Mater., 2019, 2, 193-200.
[35]	J.-H. Liu, L.-M. Yang, E. Ganz, J. Mater. Chem. A, 2019, 7, 11944-11952.
[36]	J.-H. Liu, L.-M. Yang, E. Ganz, J. Mater. Chem. A, 2019, 7, 3805-3814.
[37]	J.-H. Liu, L.-M. Yang, E. Ganz, ACS Sustainable Chem. Eng., 2018, 6, 15494-15502.
[38]	A. A. Peterson, Top. Catal., 2014, 57, 40-53.
[39]	Y. Yang, S. Zhang, K. D. Ranasinghe, O. Isayev, A. E. Roitberg, Annu. Rev. Phys. Chem., 2024, 75, 371-395.
[40]	Y. Wu, X. Li, R. Guo, R. Xu, M.-G. Ju, J. Wang, Natl. Sci. Rev., 2025, 12, nwaf081.
[41]	F.-T. Wang, J. Cheng, Curr. Opin. Electrochem., 2025, 49, 101605.
[42]	M. Suvarna, J. Pérez-Ramírez, Nat. Catal., 2024, 7, 624-635.
[43]	Z. Lian, F. Dattila, N. López, Nat. Catal., 2024, 7, 401-411.
[44]	L. Chanussot, A. Das, S. Goyal, T. Lavril, M. Shuaibi, M. Riviere, K. Tran, J. Heras-Domingo, C. Ho, W. Hu, A. Palizhati, A. Sriram, B. Wood, J. Yoon, D. Parikh, C. L. Zitnick, Z. Ulissi, ACS Catal., 2021, 11, 13062-13065.
[45]	R. Tran, J. Lan, M. Shuaibi, B. M. Wood, S. Goyal, A. Das, J. Heras-Domingo, A. Kolluru, A. Rizvi, N. Shoghi, A. Sriram, F. Therrien, J. Abed, O. Voznyy, E. H. Sargent, Z. Ulissi, C. L. Zitnick, ACS Catal., 2023, 13, 3066-3084.
[46]	K. Schütt, O. Unke, M. Gastegger, International Conference on Machine Learning, 2021, 139, 9377-9388.
[47]	K. Schütt, P.-J. Kindermans, H. E. Sauceda Felix, S. Chmiela, A. Tkatchenko, K.-R. Müller, Advances in neural information processing systems, 2017, 30, 992-1002.
[48]	Y.-L. Liao, B. Wood, A. Das, T. Smidt, International Conference on Learning Representations, 2024, 39282-39309.
[49]	J. Gasteiger, J. Groß, S. Günnemann, In International Conference on Learning Representations, 2020.
[50]	J. Gasteiger, F. Becker, S. Günnemann, Advances in neural information processing systems, 2021, 34, 6790-6802.
[51]	T. Chen, C. Guestrin, In 22nd acm sigkdd International Conference on Knowledge Discovery and Data Mining, 2016, 785-794.
[52]	L. Breiman, Mach. Learn., 2001, 45, 5-32.
[53]	J. Wei, X. Chu, X.-Y. Sun, K. Xu, H.-X. Deng, J. Chen, Z. Wei, M. Lei, InfoMat, 2019, 1, 338-358.
[54]	J. Lan, A. Palizhati, M. Shuaibi, B. M. Wood, B. Wander, A. Das, M. Uyttendaele, C. L. Zitnick, Z. W. Ulissi, npj Comput. Mater., 2023, 9, 172.
[55]	J. Chen, X. Huang, C. Hua, Y. He, P. Schwaller, Nat. Commun., 2025, 16, 3232.
[56]	B. Wander, M. Shuaibi, J. R. Kitchin, Z. W. Ulissi, C. L. Zitnick, ACS Catal., 2025, 15, 5283-5294.
[57]	C. M. Clausen, J. Rossmeisl, Z. W. Ulissi, J. Phys. Chem. C, 2024, 128, 11190-11195.
[58]	K. Tran, Z. W. Ulissi, Nat. Catal., 2018, 1, 696-703.
[59]	Z. Sun, H. Yin, K. Liu, S. Cheng, G. K. Li, S. Kawi, H. Zhao, G. Jia, Z. Yin, SmartMat, 2022, 3, 68-83.
[60]	P. Pisal, O. Krejčí, P. Rinke, npj Comput. Mater., 2025, 11, 213.
[61]	D. H. Mok, H. Li, G. Zhang, C. Lee, K. Jiang, S. Back, Nat. Commun., 2023, 14, 7303.
[62]	Y. Guo, X. He, Y. Su, Y. Dai, M. Xie, S. Yang, J. Chen, K. Wang, D. Zhou, C. Wang, J. Am. Chem. Soc., 2021, 143, 5755-5762.
[63]	M. Zhong, K. Tran, Y. Min, C. Wang, Z. Wang, C.-T. Dinh, P. De Luna, Z. Yu, A. S. Rasouli, P. Brodersen, S. Sun, O. Voznyy, C.-S. Tan, M. Askerka, F. Che, M. Liu, A. Seifitokaldani, Y. Pang, S.-C. Lo, A. Ip, Z. Ulissi, E. H. Sargent, Nature, 2020, 581, 178-183.
[64]	X. Ma, Z. Li, L. E. K. Achenie, H. Xin, J. Phys. Chem. Lett., 2015, 6, 3528-3533.
[65]	M. Andersen, K. Reuter, Acc. Chem. Res., 2021, 54, 2741-2749.
[66]	K. Ranka, S. Ke, C. Fan, J. B. Neaton, P. Agbo, F. A. Houle, ACS Electrochem., 2025, 1, 1766-1775.
[67]	Z. Liu, Y.-F. Sun, Y.-S. Wang, W. Zhang, L.-H. Gan, X.-H. Liu, L.-B. Zhao, ACS Catal., 2025, 15, 7993-8004.
[68]	S.-Q. Xiang, J.-L. Shi, S.-T. Gao, W. Zhang, L.-B. Zhao, ACS Catal., 2021, 11, 2422-2434.
[69]	S.-T. Gao, S.-Q. Xiang, J.-L. Shi, W. Zhang, L.-B. Zhao, Phys. Chem. Chem. Phys., 2020, 22, 9607-9615.
[70]	W. Hu, B. Song, L. Yang, Energy Environ. Mater., 2025, 8, e12888.
[71]	G. Kresse, D. Joubert, Phys. Rev. B, 1999, 59, 1758-1775.
[72]	G. Kresse, J. Furthmüller, Comput. Mater. Sci., 1996, 6, 15-50.
[73]	J. P. Perdew, K. Burke, M. Ernzerhof, Phys. Rev. Lett., 1996, 77, 3865-3868.
[74]	J. P. Perdew, J. A. Chevary, S. H. Vosko, K. A. Jackson, M. R. Pederson, D. J. Singh, C. Fiolhais, Phys. Rev. B, 1992, 46, 6671-6687.
[75]	K. Mathew, R. Sundararaman, K. Letchworth-Weaver, T. A. Arias, R. G. Hennig, J. Chem. Phys., 2014, 140, 084106.
[76]	K. Mathew, V. S. Chaitanya Kolluru, S. Mula, S. N. Steinmann, R. G. Hennig, J. Chem. Phys., 2019, 151, 234101.
[77]	V. Wang, N. Xu, J.-C. Liu, G. Tang, W.-T. Geng, Comput. Phys. Commun., 2021, 267, 108033.
[78]	P. Virtanen, R. Gommers, T. E. Oliphant, M. Haberland, T. Reddy, D. Cournapeau, E. Burovski, P. Peterson, W. Weckesser, J. Bright, S. J. van der Walt, M. Brett, J. Wilson, K. J. Millman, N. Mayorov, A. R. J. Nelson, E. Jones, R. Kern, E. Larson, C. J. Carey, İ. Polat, Y. Feng, E. W. Moore, J. VanderPlas, D. Laxalde, J. Perktold, R. Cimrman, I. Henriksen, E. A. Quintero, C. R. Harris, A. M. Archibald, A. H. Ribeiro, F. Pedregosa, P. van Mulbregt, S. Contributors, A. Vijaykumar, A. P. Bardelli, A. Rothberg, A. Hilboll, A. Kloeckner, A. Scopatz, A. Lee, A. Rokem, C. N. Woods, C. Fulton, C. Masson, C. Häggström, C. Fitzgerald, D. A. Nicholson, D. R. Hagen, D. V. Pasechnik, E. Olivetti, E. Martin, E. Wieser, F. Silva, F. Lenders, F. Wilhelm, G. Young, G. A. Price, G.-L. Ingold, G. E. Allen, G. R. Lee, H. Audren, I. Probst, J. P. Dietrich, J. Silterra, J. T. Webber, J. Slavič, J. Nothman, J. Buchner, J. Kulick, J. L. Schönberger, J. V. de Miranda Cardoso, J. Reimer, J. Harrington, J. L. C. Rodríguez, J. Nunez-Iglesias, J. Kuczynski, K. Tritz, M. Thoma, M. Newville, M. Kümmerer, M. Bolingbroke, M. Tartre, M. Pak, N. J. Smith, N. Nowaczyk, N. Shebanov, O. Pavlyk, P. A. Brodtkorb, P. Lee, R. T. McGibbon, R. Feldbauer, S. Lewis, S. Tygier, S. Sievert, S. Vigna, S. Peterson, S. More, T. Pudlik, T. Oshima, T. J. Pingel, T. P. Robitaille, T. Spura, T. R. Jones, T. Cera, T. Leslie, T. Zito, T. Krauss, U. Upadhyay, Y. O. Halchenko, Y. Vázquez-Baeza, Nat. Meth., 2020, 17, 261-272.
[79]	S. P. Ong, W. D. Richards, A. Jain, G. Hautier, M. Kocher, S. Cholia, D. Gunter, V. L. Chevrier, K. A. Persson, G. Ceder, Comput. Mater. Sci., 2013, 68, 314-319.
[80]	D.-J. Su, S.-Q. Xiang, S.-T. Gao, Y. Jiang, X. Liu, W. Zhang, L.-B. Zhao, Z.-Q. Tian, JACS Au, 2023, 3, 905-918.
[81]	Y.-F. Sun, Z. Liu, X. Liu, L.-H. Gan, W. Zhang, X. Zhao, L.-B. Zhao, Appl. Surf. Sci., 2025, 681, 161469.
[82]	B. Zijlstra, X. Zhang, J.-X. Liu, I. A. W. Filot, Z. Zhou, S. Sun, E. J. M. Hensen, Electrochim. Acta, 2020, 335, 135665.
[83]	K. P. Kuhl, E. R. Cave, D. N. Abram, T. F. Jaramillo, Energy Environ. Sci., 2012, 5, 7050-7059.
[84]	Y. Hori, A. Murata, R. Takahashi, J. Chem. Soc., araday Trans. 1, 1989, 85, 2309-2326.
[85]	J. Li, Q. Zhu, A. Chang, S. Cheon, Y. Gao, B. Shang, H. Li, C. L. Rooney, L. Ren, Z. Jiang, Y. Liang, Z. Feng, S. Yang, L. R. Baker, H. Wang, Nat. Nanotechnol., 2025, 20, 515-522.
[86]	W. Sun, S. T. Dacek, S. P. Ong, G. Hautier, A. Jain, W. D. Richards, A. C. Gamst, K. A. Persson, G. Ceder, Sci. Adv., 2016, 2, e1600225.
[87]	H. Chen, G. Hautier, G. Ceder, J. Am. Chem. Soc., 2012, 134, 19619-19627.
[88]	A. A. Peterson, F. Abild-Pedersen, F. Studt, J. Rossmeisl, J. K. Nørskov, Energy Environ. Sci., 2010, 3, 1311-1315.
[89]	X. Nie, M. R. Esopi, M. J. Janik, A. Asthagiri, Angew. Chem. Int. Ed., 2013, 52, 2459-2462.
[90]	A. Guan, Q. Wang, Y. Ji, S. Li, C. Yang, L. Qian, L. Zhang, L. Wu, G. Zheng, J. Mater. Chem. A, 2021, 9, 21779-21784.

机器学习加速的二氧化碳全局反应网络动力学模拟

Machine learning-accelerated electrocatalytic microkinetic model for global reaction network simulation of CO₂ reduction reaction

RichHTML

PDF

Supporting Information

可视化

摘要/Abstract

引用本文

使用本文

扫码分享

图/表 6

参考文献

相关文章 15

编辑推荐

Metrics

[1]	王淼, 沈天宇, 周恒, 杨帅康, 郝风坤, 王超辉, Mohan Kumar, 铁祚庥, 陈双明, 范战西, 金钟. 中性介质中利用原子级精准富羰基金属团簇直接电催化还原硝酸盐合成氨[J]. 催化学报, 2026, 88(9): 259-268.
[2]	秦家祥, 张松培, 李星局, 陈鑫泰, 赵佳, 牟效玲, 宋宪根, 严丽, 林荣和, 丁云杰. 丁二烯半加氢反应中Pd-Ag纳米催化剂结构-选择性耦合关系解析[J]. 催化学报, 2026, 87(8): 342-352.
[3]	何朝春, 刘纯莉, 柳雍华, 王涛. 均相催化合成氨中兼顾N≡N键活化与N-H键形成的理论构效关系[J]. 催化学报, 2026, 87(8): 316-326.
[4]	王森, 李诗颖, 耿蕊, 周博, 王鹏飞, 秦张峰, 董梅, 王建国, 樊卫斌. 引发二氧化碳选择性加氢制甲烷的最小镍物种: 限域在MFI结构中的镍二聚体以及锰氧化物的增强作用[J]. 催化学报, 2026, 87(8): 113-125.
[5]	丁扬, 陆义桢, 余天荣, 张明睿, 赵睿, 杨睿杰, 李其鑫, 吴仕群, 张金龙. 光驱动费托合成中双路径策略实现高选择性烃类产物制备[J]. 催化学报, 2026, 87(8): 22-46.
[6]	李博文, 林长霖, 王琪, 伦永烽, 方俊, 宋树芹, 王毅. 配体-壳层工程设计Au₂₅纳米团簇加速CO₂电还原生成比例可调的合成气[J]. 催化学报, 2026, 87(8): 396-409.
[7]	周亚太, 袁成成, 夏伟, 王骏, 朱潇锋, 张勇, 朱必成, 余家国. 通过掺杂优化CdS/ZnO S型异质结界面电子转移和表面氢吸附的理论计算研究[J]. 催化学报, 2026, 86(7): 327-337.
[8]	Shahla Karimi, Mehran Rezaei, 邓积光, 戴洪兴, Ali Rastegarpanah. 甲烷分解用贵金属基催化剂的最新进展: 性能、机理与优化[J]. 催化学报, 2026, 86(7): 9-48.
[9]	刘家英, 方煜. 揭示二维共价有机框架光催化的结构-活性关系: 从分子工程到高性能优化[J]. 催化学报, 2026, 85(6): 47-87.
[10]	陈兰兰, 圣利, 周亚男, 罗其全, 李震宇, 张文华, 杨金龙. Ni₁-MoS₂单原子催化剂用于CO₂加氢制甲醇的优越性: 密度泛函理论与微观动力学研究[J]. 催化学报, 2026, 85(6): 143-152.
[11]	叶艺涵, 丁一伦, 彭涛, 刘成, 李昕哲, 赵勇智, 肖建平, 焦峰, 潘秀莲. 分子筛内累积碳物种的动态限域作用[J]. 催化学报, 2026, 84(5): 74-79.
[12]	吴静尧, 吕昱静, 赵强, 王朔, 汪颖, 温娜, 丁正新, 张子重, 龙金林. 共价有机框架的电子-质子双重奏: 高效催化氧气直接还原生成过氧化氢[J]. 催化学报, 2026, 84(5): 288-300.
[13]	刘芷瑶, 刘唐康, 秦川, 刘国亮, 郑安民. 氧化锆介导界面催化在CO₂加氢反应中的研究进展[J]. 催化学报, 2026, 84(5): 1-24.
[14]	邢肖肖, 吴沛文, 邹义儒, 高兆增, 俞镇东, 唐旻萌, 巢艳红, 朱文帅, 刘植昌, 徐春明. 双相界面工程: 一种机器学习指导的优化FCC油浆选择性氧化脱硫策略[J]. 催化学报, 2026, 84(5): 375-389.
[15]	顾宇, 张淑嘉, 徐铭潞, 闫昊, 周铭昊, 王磊, 施慧. 甲烷的脱氢芳构化及其与丙烷共芳构化: 反应机理、催化剂设计、积碳和过程强化[J]. 催化学报, 2026, 84(5): 25-60.

机器学习加速的二氧化碳全局反应网络动力学模拟

Machine learning-accelerated electrocatalytic microkinetic model for global reaction network simulation of CO2 reduction reaction

RichHTML

PDF

Supporting Information

可视化

摘要/Abstract

引用本文

使用本文

扫码分享

图/表 6

参考文献

相关文章 15

编辑推荐

Metrics

Machine learning-accelerated electrocatalytic microkinetic model for global reaction network simulation of CO₂ reduction reaction