[1] [1] MAC DOWELL N, FENNELL P S, SHAH N, et al. The role of CO2 capture and utilization in mitigating climate change［J］. Nat Clim Chang, 2017, 7(4): 243-249.

[2] [2] CAI Y, SAM C Y, CHANG T. Nexus between clean energy consumption, economic growth and CO2 emissions［J］. J Clean Prod, 2018, 182: 1001-1011.

[3] [3] QIAO W, LU H, ZHOU G, et al. A hybrid algorithm for carbon dioxide emissions forecasting based on improved lion swarm optimizer［J］. J Clean Prod, 2020, 244: 118612.

[4] [4] ANDERSON T R, HAWKINS E, JONES P D. CO2, the greenhouse effect and global warming: from the pioneering work of Arrhenius and Callendar to today’s Earth System Models［J］. Endeavour, 2016, 40(3): 178-187.

[5] [5] LOVELOCK C E, CAHOON D R, FRIESS D A, et al. The vulnerability of Indo-Pacific mangrove forests to sea-level rise［J］. Nature, 2015, 526(7574): 559-563.

[6] [6] HIMANEN L, GEURTS A, FOSTER A S, et al. Data-driven materials science: status, challenges, and perspectives［J］. Adv Sci, 2019, 6(21): 1900808.

[7] [7] RACCUGLIA P, ELBERT K C, ADLER P D F, et al. Machine-learning-assisted materials discovery using failed experiments［J］. Nature, 2016, 533(7601): 73-76.

[8] [8] LI B, DUAN Y, LUEBKE D, et al. Advances in CO2 capture technology: A patent review［J］. Appl Energy, 2013, 102: 1439-1447.

[9] [9] CHU S, CUI Y, LIU N. The path towards sustainable energy［J］. Nat Mater, 2016, 16(1): 16-22.

[10] [10] BOTU V, RAMPRASAD R. Learning scheme to predict atomic forces and accelerate materials simulations［J］. Phys Rev B, 2015, 92(9): 94306.

[11] [11] LI Z, LI H, SHAO L. Improving online customer shopping experience with computer vision and machine learning methods［J］. Lect Notes Comput Sci, 2016, 9751: 427-436.

[12] [12] KIM K jae, AHN H. A recommender system using GA K-means clustering in an online shopping market［J］. Expert Syst Appl, 2008, 34(2): 1200-1209.

[13] [13] SAMANT P, AGARWAL R. Machine learning techniques for medical diagnosis of diabetes using iris images［J］. Comput Methods Programs Biomed, 2018, 157: 121-128.

[14] [14] WU M, CHEN L. Image recognition based on deep learning［J］. Proc - 2015 Chinese Autom Congr CAC, 2015, 2016: 542-546.

[15] [15] LIPPMANN R. An introduction to computing with neural nets［J］. IEEE Assp Mag, 1987, 4(2): 4-22.

[16] [16] LECUN Y, BENGIO Y, HINTON G. Deep learning［J］. Nature, 2015, 521(7553): 436-444.

[17] [17] ZHANG C, BENGIO S, HARDT M, et al. Understanding deep learning (still) requires rethinking generalization［J］. Communications of the ACM, 2021, 64(3): 107-115.

[20] [20] DEBE M K. Electrocatalyst approaches and challenges for automotive fuel cells［J］. Nature, 2012, 486(7401): 43-51.

[21] [21] GHIABI C, GHAFFARINEJAD A, KAZEMI H, et al. In situ, one-step and co-electrodeposition of graphene supported dendritic and spherical nano-palladium-silver bimetallic catalyst on carbon cloth for electrooxidation of methanol in alkaline media［J］. Renew Energy, 2018, 126: 1085-1092.

[22] [22] MEHMETI A, MCPHAIL S J, PUMIGLIA D, et al. Life cycle sustainability of solid oxide fuel cells: From methodological aspects to system implications［J］. J Power Sources, 2016, 325: 772-785.

[23] [23] CASCOS V, FERNNDEZ-DAZ M T, ALONSO J A. Structural and electrical characterization of the novel SrCo1-xTixO3-x (x = 0.05, 0.1 and 0.15) perovskites: Evaluation as cathode materials in solid oxide fuel cells［J］. Renew Energy, 2019, 133: 205-215.

[24] [24] ARRIAGADA J, OLAUSSON P, SELIMOVIC A. Artificial neural network simulator for SOFC performance prediction［J］. J Power Sources, 2002, 112(1): 54-60.

[25] [25] KANG Y W, LI J, CAO G Y, et al. Dynamic temperature modeling of an SOFC using least squares support vector machines［J］. J Power Sources, 2008, 179(2): 683-692.

[26] [26] WU X J, HUANG Q, ZHU X J. Thermal modeling of a solid oxide fuel cell and micro gas turbine hybrid power system based on modified LS-SVM［J］. Int J Hydrogen Energy, 2011, 36(1): 885-892.

[27] [27] CHEN J, CHEN Y, ZHANG H. Study on Fuel Utilization Dynamic model of a SOFC-GT Hybrid System Based on Deep Learning Technique［C］//E3S Web of Conferences. EDP Sciences, 2019, 113: 1-6.

[28] [28] SHAO Q, GAO E, MARA T, et al. Global sensitivity analysis of solid oxide fuel cells with Bayesian sparse polynomial chaos expansions［J］. Appl Energy, 2020, 260: 114318.

[29] [29] SONG S, XIONG X, WU X, et al. Modeling the SOFC by BP neural network algorithm［J］. Int J Hydrogen Energy, 2021, 46(38): 20065-20077.

[30] [30] XU H, MA J, TAN P, et al. Towards online optimisation of solid oxide fuel cell performance: Combining deep learning with multi-physics simulation［J］. Energy AI, 2020, 1: 100003.

[31] [31] LI Y, SHEN J, LU J. Constrained model predictive control of a solid oxide fuel cell based on genetic optimization［J］. J Power Sources, 2011, 196(14): 5873-5880.

[32] [32] NASSEF A M, FATHY A, SAYED E T, et al. Maximizing SOFC performance through optimal parameters identification by modern optimization algorithms［J］. Renew Energy, 2019, 138: 458-464.

[33] [33] GHORBANI B, VIJAYARAGHAVAN K. Developing a virtual hydrogen sensor for detecting fuel starvation in solid oxide fuel cells using different machine learning algorithms［J］. Int J Hydrogen Energy, 2020, 45(51): 27730-27744.

[34] [34] WANG Y, WU C, ZHAO S, et al. Coupling deep learning and multi-objective genetic algorithms to achieve high performance and durability of direct internal reforming solid oxide fuel cell［J］. Appl Energy, 2022, 315: 119046.

[35] [35] LI S, CAO H, YANG Y. Data-driven simultaneous fault diagnosis for solid oxide fuel cell system using multi-label pattern identification［J］. J Power Sources, 2018, 378(1): 646-659.

[36] [36] MENG-TING C, XIAO-WEI F, ZHONG-HUA D, et al. Data-Driven Fault Detection for SOFC system based on Random Forest and SVM［C］//2019 Chinese Automation Congress (CAC). IEEE, 2019: 2829-2834.

[37] [37] ZHANG Z, LI S, XIAO Y, et al. Intelligent simultaneous fault diagnosis for solid oxide fuel cell system based on deep learning［J］. Appl Energy, 2019, 233-234: 930-942.

[38] [38] ZHENG Y, WU X long, ZHAO D, et al. Data-driven fault diagnosis method for the safe and stable operation of solid oxide fuel cells system［J］. J Power Sources, 2021, 490: 229561.

[39] [39] SEVERSON K A, ATTIA P M, JIN N, et al. Data-driven prediction of battery cycle life before capacity degradation［J］. Nat Energy, 2019, 4(5): 383-391.

[40] [40] NUHIC A, TERZIMEHIC T, SOCZKA-GUTH T, et al. Health diagnosis and remaining useful life prognostics of lithium-ion batteries using data-driven methods［J］. J Power Sources, 2013, 239: 680-688.

[41] [41] PATIL M A, TAGADE P, HARIHARAN K S, et al. A novel multistage Support Vector Machine based approach for Li ion battery remaining useful life estimation［J］. Appl Energy, 2015, 159: 285-297.

[42] [42] ZHANG Y Y, TANG Q, ZHANG Y Y, et al. Identifying degradation patterns of lithium ion batteries from impedance spectroscopy using machine learning［J］. Nat Commun, 2020, 11(1): 1-6.

[43] [43] TRAN M K, PANCHAL S, CHAUHAN V, et al. Python-based scikit-learn machine learning models for thermal and electrical performance prediction of high-capacity lithium-ion battery［J］. Int J Energy Res, 2022, 46(2): 786-794.

[44] [44] ZHAO Y, LIU P, WANG Z, et al. Fault and defect diagnosis of battery for electric vehicles based on big data analysis methods［J］. Appl Energy, 2017, 207: 354-362.

[45] [45] WU J, ZHANG C, CHEN Z. An online method for lithium-ion battery remaining useful life estimation using importance sampling and neural networks［J］. Appl Energy, 2016, 173: 134-140.

[46] [46] CHEMALI E, KOLLMEYER P J, PREINDL M, et al. State-of-charge estimation of Li-ion batteries using deep neural networks: A machine learning approach［J］. J Power Sources, 2018, 400: 242-255.

[47] [47] ZAHID T, XU K, LI W, et al. State of charge estimation for electric vehicle power battery using advanced machine learning algorithm under diversified drive cycles［J］. Energy, 2018, 162: 871-882.

[48] [48] SHEN S, NEMANI V, LIU J, et al. A hybrid machine learning model for battery cycle life prediction with early cycle data［C］//2020 IEEE Transportation Electrification Conference & Expo (ITEC). IEEE, 2020: 181-184.

[49] [49] HOUCHINS G, VISWANATHAN V. An accurate machine-learning calculator for optimization of Li-ion battery cathodes［J］. J Chem Phys, 2020, 153(5): 054124.

[50] [50] JIANG Z, LI J, YANG Y, et al. Machine-learning-revealed statistics of the particle-carbon/binder detachment in lithium-ion battery cathodes［J］. Nat Commun, 2020, 11(1): 1-9.

[51] [51] JOSHI R P, EICKHOLT J, LI L, et al. Machine learning the voltage of electrode materials in metal-ion batteries［J］. ACS Appl Mater Interfaces, 2019, 11(20): 18494-18503.

[52] [52] SHEN S, SADOUGHI M, CHEN X, et al. A deep learning method for online capacity estimation of lithium-ion batteries［J］. J Energy Storage, 2019, 25: 100817.

[53] [53] WARD L, AGRAWAL A, CHOUDHARY A, et al. A general-purpose machine learning framework for predicting properties of inorganic materials［J］. npj Comput Mater, 2016, 2(1): 1-7.

[54] [54] MOSES I A, JOSHI R P, OZDEMIR B, et al. Machine learning screening of metal-ion battery electrode materials［J］. ACS Appl Mater Interfaces, 2021, 13(45): 53355-53362.

[55] [55] ZHOU L, YAO A M, WU Y, et al. Machine Learning Assisted Prediction of Cathode Materials for Zn-Ion Batteries［J］. Adv Theory Simulations, 2021, 4(9): 1-6.

[56] [56] HONG C, ZHANG Q, HE K, et al. Variations of China’s emission estimates: Response to uncertainties in energy statistics［J］. Atmos Chem Phys, 2017, 17(2): 1227-1239.

[57] [57] MICHAEL K, GOLAB A, SHULAKOVA V, et al. Geological storage of CO2 in saline aquifers-A review of the experience from existing storage operations［J］. Int J Greenh Gas Control, 2010, 4(4): 659-667.

[58] [58] AMINU M D, NABAVI S A, ROCHELLE C A, et al. A review of developments in carbon dioxide storage［J］. Appl Energy, 2017, 208: 1389-1419.

[59] [59] FU H Q, ZHANG L, ZHENG L R, et al. Enhanced CO2 electroreduction performance over Cl-modified metal catalysts［J］. J Mater Chem A, 2019, 7(20): 12420-12425.

[60] [60] QIAO J, LIU Y, HONG F, et al. A review of catalysts for the electroreduction of carbon dioxide to produce low-carbon fuels［J］. Chem Soc Rev, 2014, 43(2).631-675.

[61] [61] HOANG T T H, VERMA S, MA S, et al. Nanoporous Copper-Silver Alloys by Additive-Controlled Electrodeposition for the Selective Electroreduction of CO2 to Ethylene and Ethanol［J］. J Am Chem Soc, 2018, 140(17): 5791-5797.

[62] [62] CHEN A, ZHANG X, CHEN L, et al. A machine learning model on simple features for CO2 reduction electrocatalysts［J］. J Phys Chem C, 2020.22471-22478.

[63] [63] MA X, LI Z, ACHENIE L E K, et al. Machine-learning-augmented chemisorption model for CO2 electroreduction catalyst screening［J］. J Phys Chem Lett, 2015, 6(18): 3528-3533.

[64] [64] LI Z, MA X, XIN H. Feature engineering of machine-learning chemisorption models for catalyst design［J］. Catal today, 2017, 280(2): 232-238.

[65] [65] ULISSI Z W, TANG M T, XIAO J, et al. Machine-learning methods enable exhaustive searches for active Bimetallic facets and reveal active site motifs for CO2 reduction［J］. ACS Catal, 2017, 7(10): 6600-6608.

[66] [66] CHEN Y, HUANG Y, CHENG T, et al. Identifying active sites for CO2 reduction on dealloyed gold surfaces by combining machine learning with multiscale simulations［J］. J Am Chem Soc, 2019, 141(29): 11651-11657.

[67] [67] GUSAROV S, STOYANOV S R, SIAHROSTAMI S. Development of fukui function based descriptors for a machine learning study of CO2 reduction［J］. J Phys Chem C, 2020, 124(18): 10079-10084.