Theoretical High-Throughput Screening of Single-Atom CO2 Electroreduction Catalysts to Methanol Using Active Learning

Honghao Chen; Jun Yin; Jiali Li; Xiaonan Wang

doi:10.1016/j.eng.2025.03.039

Engineering ›› 2025, Vol. 52 ›› Issue (9) :172 -182. DOI: 10.1016/j.eng.2025.03.039

Research

Article

Theoretical High-Throughput Screening of Single-Atom CO₂ Electroreduction Catalysts to Methanol Using Active Learning

Author information +

History +

PDF (3493KB)

Abstract

Industrial decarbonization is critical for achieving net-zero goals. The carbon dioxide electrochemical reduction reaction (CO₂RR) is a promising approach for converting CO₂ into high-value chemicals, offering the potential for decarbonizing industrial processes toward a sustainable, carbon-neutral future. However, developing CO₂RR catalysts with high selectivity and activity remains a challenge due to the complexity of finding such catalysts and the inefficiency of traditional computational or experimental approaches. Here, we present a methodology integrating density functional theory (DFT) calculations, deep learning models, and an active learning strategy to rapidly screen high-performance catalysts. The proposed methodology is then demonstrated on graphene-based single-atom catalysts for selective CO₂ electroreduction to methanol. First, we conduct systematic binding energy calculations for 3045 single-atom catalysts to identify thermodynamically stable catalysts as the design space. We then use a graph neural network, fine-tuned with a specialized adsorption energy database, to predict the relative activity and selectivity of the candidate catalysts. An autonomous active learning framework is used to facilitate the exploration of designs. After six learning cycles and 2180 adsorption calculations across 15 intermediates, we develop a surrogate model that identifies four novel catalysts on the Pareto front of activity and selectivity. Our work demonstrates the effectiveness of leveraging a domain foundation model with an active learning framework and holds potential to significantly accelerate the discovery of high-performance CO₂RR catalysts.

Keywords

CO₂ electrochemical reduction / Machine learning / Active learning / Catalyst / Decarbonization

Cite this article

Download citation ▾

Honghao Chen, Jun Yin, Jiali Li, Xiaonan Wang. Theoretical High-Throughput Screening of Single-Atom CO₂ Electroreduction Catalysts to Methanol Using Active Learning. Engineering, 2025, 52(9): 172-182 DOI:10.1016/j.eng.2025.03.039

登录浏览全文

4963

注册一个新账户忘记密码

References

Publishing order | Descend order by publishing year | Descend order by cited within

[1]	Meinshausen M, Lewis J, McGlade C, Gütschow J, Nicholls Z, Burdon R, et al. Realization of Paris Agreement pledges may limit warming just below 2 °C. Nature 2022; 604(7905):304-9.

[2]	Gao W, Liang S, Wang R, Jiang Q, Zhang Y, Zheng Q, et al. Industrial carbon dioxide capture and utilization: state of the art and future challenges. Chem Soc Rev 2020; 49(23):8584-686.

[3]	Jin S, Hao Z, Zhang K, Yan Z, Chen J. Advances and challenges for the electrochemical reduction of CO₂ to CO: from fundamentals to industrialization. Angew Chem 2021; 133(38):20795-816.

[4]	Xu D, Li K, Jia B, Sun W, Zhang W, Liu X, et al. Electrocatalytic CO₂ reduction towards industrial applications. Carbon Energy 2023; 5(1):e230.

[5]	Lin R, Guo J, Li X, Patel P, Seifitokaldani A. Electrochemical reactors for CO₂ conversion. Catalysts 2020; 10(5):473.

[6]	Kibria MG, Edwards JP, Gabardo CM, Dinh CT, Seifitokaldani A, Sinton D, et al. Electrochemical CO₂ reduction into chemical feedstocks: from mechanistic electrocatalysis models to system design. Adv Mater 2019; 31(31):1807166.

[7]	Zhang J, Cai W, Hu FX, Yang H, Liu B. Recent advances in single atom catalysts for the electrochemical carbon dioxide reduction reaction. Chem Sci 2021; 12 (20):6800-19.

[8]	Tan X, Yu C, Ren Y, Cui S, Li W, Qiu J. Recent advances in innovative strategies for the CO₂ electroreduction reaction. Energy Environ Sci 2021; 14(2):765-80.

[9]	Li M, Wang H, Luo W, Sherrell PC, Chen J, Yang J. Heterogeneous single-atom catalysts for electrochemical CO₂ reduction reaction. Adv Mater 2020; 32 (34):2001848.

[10]	Wei J, Chu X, Sun XY, Xu K, Deng HX, Chen J, et al. Machine learning in materials science. InfoMat 2019; 1(3):338-58.

[11]	Cai J, Chu X, Xu K, Li H, Wei J. Machine learning-driven new material discovery. Nanoscale Adv 2020; 2(8):3115-30.

[12]	Duan C, Nandy A, Kulik HJ. Machine learning for the discovery, design, and engineering of materials. Annu Rev Chem Biomol Eng 2022; 13(1):405-29.

[13]	Yan Y, Borhani TN, Subraveti SG, Pai KN, Prasad V, Rajendran A, et al. Harnessing the power of machine learning for carbon capture, utilisation, and storage (CCUS)—a state-of-the-art review. Energy Environ Sci 2021; 14 (12):6122-57.

[14]	Zhong M, Tran K, Min Y, Wang C, Wang Z, Dinh CT, et al. Accelerated discovery of CO₂ electrocatalysts using active machine learning. Nature 2020; 581 (7807):178-83.

[15]	Chen Y, Huang Y, Cheng T, Goddard WA III. Identifying active sites for CO₂ reduction on dealloyed gold surfaces by combining machine learning with multiscale simulations. J Am Chem Soc 2019; 141(29):11651-7.

[16]	Cheng D, Zhao ZJ, Zhang G, Yang P, Li L, Gao H, et al. The nature of active sites for carbon dioxide electroreduction over oxide-derived copper catalysts. Nat Commun 2021; 12:395.

[17]	Chen A, Zhang X, Chen L, Yao S, Zhou Z. A machine learning model on simple features for CO₂ reduction electrocatalysts. J Phys Chem C 2020; 124 (41):22471-8.

[18]	Wan X, Zhang Z, Niu H, Yin Y, Kuai C, Wang J, et al. Machine-learningaccelerated catalytic activity predictions of transition metal phthalocyanine dual-metal-site catalysts for CO₂ reduction. J Phys Chem Lett 2021; 12 (26):6111-8.

[19]	Hu E, Liu C, Zhang W, Yan Q. Machine learning assisted understanding and discovery of CO₂ reduction reaction electrocatalyst. J Phys Chem C 2023; 127 (2):882-93.

[20]	Tamtaji M, Chen S, Hu Z, Goddard III WA, Chen G. A surrogate machine learning model for the design of single-atom catalyst on carbon and porphyrin supports towards electrochemistry. J Phys Chem C 2023; 127(21):9992-10000.

[21]	Kresse G, Furthmüller J. Efficient iterative schemes for ab initio total-energy calculations using a plane-wave basis set. Phys Rev B 1996; 54(16):11169-86.

[22]	Wang Y, You L, Zhou K. Origin of the N-coordinated single-atom Ni sites in heterogeneous electrocatalysts for CO₂ reduction reaction. Chem Sci 2021; 12 (42):14065-73.

[23]	Hossain MD, Huang Y, Yu TH, Goddard III WA, Luo Z. Reaction mechanism and kinetics for CO₂ reduction on nickel single atom catalysts from quantum mechanics. Nat Commun 2020; 11:2256.

[24]	Wang S, Li L, Li J, Yuan C, Kang Y, Hui KS, et al. High-throughput screening of nitrogen-coordinated bimetal catalysts for multielectron reduction of CO₂ to CH₄ with high selectivity and low limiting potential. J Phys Chem C 2021; 125 (13):7155-65.

[25]	Nørskov JK, Rossmeisl J, Logadottir A, Lindqvist L, Kitchin JR, Bligaard T, et al. Origin of the overpotential for oxygen reduction at a fuel-cell cathode. J Phys Chem B 2004; 108(46):17886-92.

[26]	Kresse G, Joubert D. From ultrasoft pseudopotentials to the projector augmented-wave method. Phys Rev B 1999; 59(3):1758-75.

[27]	Blöchl PE. Projector augmented-wave method. Phys Rev B 1994; 50 (24):17953-79.

[28]	Perdew JP, Burke K, Ernzerhof M. Generalized gradient approximation made simple. Phys Rev Lett 1996; 77(18):3865-8.

[29]	Grimme S, Antony J, Ehrlich S, Krieg H. A consistent and accurate ab initio parametrization of density functional dispersion correction (DFT-D) for the 94 elements H-Pu. J Chem Phys 2010; 132(15):154104.

[30]	Perdew JP, Wang Y. Pair-distribution function and its coupling-constant average for the spin-polarized electron gas. Phys Rev B 1992; 46 (20):12947-54.

[31]	Wang V, Xu N, Liu JC, Tang G, Geng WT. VASPKIT: a user-friendly interface facilitating high-throughput computing and analysis using VASP code. Comput Phys Commun 2021; 267:108033.

[32]	Granda-Marulanda LP, Rendón-Calle A, Builes S, Illas F, Koper MTM, CalleVallejo F. A semiempirical method to detect and correct DFT-based gas-phase errors and its application in electrocatalysis. ACS Catal 2020; 10(12):6900-7.

[33]	Ong SP, Richards WD, Jain A, Hautier G, Kocher M, Cholia S, et al. Python Materials Genomics (PyMatGen): a robust, open-source python library for materials analysis. Comput Mater Sci 2013; 68:314-9.

[34]	Di Liberto G, Giordano L, Pacchioni G. Predicting the stability of single-atom catalysts in electrochemical reactions. ACS Catal 2024; 14(1):45-55.

[35]	Tran K, Ulissi ZW. Active learning across intermetallics to guide discovery of electrocatalysts for CO₂ reduction and H₂ evolution. Nat Catal 2018; 1 (9):696-703.

[36]	Kulkarni A, Siahrostami S, Patel A, Nørskov JK. Understanding catalytic activity trends in the oxygen reduction reaction. Chem Rev 2018; 118(5):2302-12.

[37]	Peterson AA, Abild-Pedersen F, Studt F, Rossmeisl J, Nørskov JK. How copper catalyzes the electroreduction of carbon dioxide into hydrocarbon fuels. Energy Environ Sci 2010; 3(9):1311-5.

[38]	Lookman T, Balachandran PV, Xue D, Yuan R. Active learning in materials science with emphasis on adaptive sampling using uncertainties for targeted design. npj Comput Mater 2019; 5:21.

[39]	Cui T, Tang C, Su M, Zhang S, Li Y, Bai L, et al. Geometry-enhanced pretraining on interatomic potentials. Nat Mach Intell 2024; 6(4):428-36.

[40]	Liao YL, Wood B, Das A, Smidt T. EquiformerV2: improved equivariant transformer for scaling to higher-degree representations. 2023. arXiv:2306.12059.

[41]	Chanussot L, Das A, Goyal S, Lavril T, Shuaibi M, Riviere M, et al. Open catalyst 2020 (OC20) dataset and community challenges ACS Catal 2021; 11 (10):6059-72.

[42]	Tran R, Lan J, Shuaibi M, Wood BM, Goyal S, Das A, et al. The open catalyst 2022 (OC22) dataset and challenges for oxide electrocatalysts) ACS Catal 2023; 13 (5):3066-84.

[43]	Dutschmann TM, Kinzel L ter Laak A, Baumann K. Large-scale evaluation of kfold cross-validation ensembles for uncertainty estimation. J Cheminform 2023; 15:49.

[44]	Suvarna M, Zou T, Chong SH, Ge Y, Martín AJ, Pérez-Ramírez J. Active learning streamlines development of high performance catalysts for higher alcohol synthesis. Nat Commun 2024; 15:5844.

[45]	Bennett JA, Orouji N, Khan M, Sadeghi S, Rodgers J, Abolhasani M. Autonomous reaction Pareto-front mapping with a self-driving catalysis laboratory. Nat Chem Eng 2024; 1(3):240-50.

[46]	Chen L, Tian Y, Hu X, Yao S, Lu Z, Chen S, et al. A universal machine learning framework for electrocatalyst innovation: a case study of discovering alloys for hydrogen evolution reaction. Adv Funct Mater 2022; 32(47): 2208418.

[47]	Wang X, Niu H, Liu Y, Shao C, Robertson J, Zhang Z, et al. Theoretical investigation on graphene-supported single-atom catalysts for electrochemical CO₂ reduction. Cat Sci Technol 2020; 10(24):8465-72.

[48]	Nitopi S, Bertheussen E, Scott SB, Liu X, Engstfeld AK, Horch S, et al. Progress and perspectives of electrochemical CO₂ reduction on copper in aqueous electrolyte. Chem Rev 2019; 119(12):7610-72.

[49]	Todorova TK, Schreiber MW, Fontecave M. Mechanistic understanding of CO₂ reduction reaction (CO2RR) toward multicarbon products by heterogeneous copper-based catalysts. ACS Catal 2020; 10(3):1754-68.

[50]	Zhang YJ, Sethuraman V, Michalsky R, Peterson AA. Competition between CO₂ reduction and H₂ evolution on transition-metal electrocatalysts. ACS Catal 2014; 4(10):3742-8.

[51]	Back S, Lim J, Kim NY, Kim YH, Jung Y. Single-atom catalysts for CO₂ electroreduction with significant activity and selectivity improvements. Chem Sci 2017; 8(2):1090-6.

[52]	Yang H, Wu Y, Li G, Lin Q, Hu Q, Zhang Q, et al. Scalable production of efficient single-atom copper decorated carbon membranes for CO₂ electroreduction to methanol. J Am Chem Soc 2019; 141(32):12717-23.

[53]	Bai X, Zhao X, Zhang Y, Ling C, Zhou Y, Wang J, et al. Dynamic stability of copper single-atom catalysts under working conditions. J Am Chem Soc 2022; 144(37):17140-8.

[54]	Cao H, Zhang Z, Chen JW, Wang YG. Potential-dependent free energy relationship in interpreting the electrochemical performance of CO₂ reduction on single atom catalysts. ACS Catal 2022; 12(11):6606-17.

[55]	Abed J, Kim J, Shuaibi M, Wander B, Duijf B, Mahesh S, et al. Open catalyst experiments 2024 (OCx24): bridging experiments and computational models. 2024. arXiv:2411.11783.

[56]	Deng B, Zhong P, Jun K, Riebesell J, Han K, Bartel CJ, et al. CHGNet as a pretrained universal neural network potential for charge-informed atomistic modelling. Nat Mach Intell 2023; 5(9):1031-41.

[57]	Zhang D, Liu X, Zhang X, Zhang C, Cai C, Bi H, et al. DPA-2: a large atomic model as a multi-task learner. npj Comput Mater 2024; 10:293.

[58]	Chang R, Wang YX, Ertekin E. Towards overcoming data scarcity in materials science: unifying models and datasets with a mixture of experts framework. npj Comput Mater 2022; 8:242.