REFERENCES
1. Canfield DE, Glazer AN, Falkowski PG. The evolution and future of Earth's nitrogen cycle. Science 2010;330:192-6.
2. Foster SL, Bakovic SIP, Duda RD, et al. Catalysts for nitrogen reduction to ammonia. Nat Catal 2018;1:490-500.
3. Rosca V, Duca M, de Groot MT, Koper MT. Nitrogen cycle electrocatalysis. Chem Rev 2009;109:2209-44.
4. Liu H. Ammonia synthesis catalyst 100 years: Practice, enlightenment and challenge. Chin J Catal 2014;35:1619-40.
5. Lehnert N, Musselman BW, Seefeldt LC. Grand challenges in the nitrogen cycle. Chem Soc Rev 2021;50:3640-6.
7. Kyriakou V, Garagounis I, Vourros A, Vasileiou E, Stoukides M. An electrochemical haber-bosch process. Joule 2020;4:142-58.
8. Chen JG, Crooks RM, Seefeldt LC, et al. Beyond fossil fuel-driven nitrogen transformations. Science 2018;360:eaar6611.
9. Yu L, Li F. Pt2 dimer anchored vertically in defective BN monolayer as an efficient catalyst for N2 reduction: a DFT Study. Catalysts 2022;12:1387.
10. Zhao X, Zhang X, Xue Z, Chen W, Zhou Z, Mu T. Fe nanodot-decorated MoS2 nanosheets on carbon cloth: an efficient and flexible electrode for ambient ammonia synthesis. J Mater Chem A 2019;7:27417-22.
11. Li S, Peng M, Song Y, et al. Screening transition metal and nonmetal atoms co-doped graphyne as efficient single-atom catalysts for nitrogen reduction. Chem Eng J 2024;495:153275.
12. Wang S, Shi L, Bai X, Li Q, Ling C, Wang J. Highly efficient photo-/electrocatalytic reduction of nitrogen into ammonia by dual-metal sites. ACS Cent Sci 2020;6:1762-71.
13. Yang X, Shang C, Zhou S, Zhao J. MBenes: emerging 2D materials as efficient electrocatalysts for the nitrogen reduction reaction. Nanoscale Horiz 2020;5:1106-15.
14. Han B, Li F. Regulating the electrocatalytic performance for nitrogen reduction reaction by tuning the N contents in Fe3@NxC20-x (x = 0~4): a DFT exploration. J Mater Inf 2023;3:24.
15. Cao J, Hu Y, Zheng Y, Zhang W, Yu B. Recent advances and challenges of nitrogen/nitrate electro catalytic reduction to ammonia synthesis. Front Energy 2024;18:128-40.
16. Ren Y, Yu C, Tan X, Huang H, Wei Q, Qiu J. Strategies to suppress hydrogen evolution for highly selective electrocatalytic nitrogen reduction: challenges and perspectives. Energy Environ Sci 2021;14:1176-93.
17. Cui Y, Ren C, Li Q, Ling C, Wang J. Hybridization state transition under working conditions: activity origin of single-atom catalysts. J Am Chem Soc 2024;146:15640-7.
18. Liu T, Jing Y, Li Y. First-principles insights into the selectivity of CO2 electroreduction over heterogeneous single-atom catalysts. J Phys Chem Lett 2024;15:6216-21.
19. Sun H, Liu J. Advancing CO2RR with O-coordinated single-atom nanozymes: a DFT and machine learning exploration. ACS Catal 2024;14:14021-30.
20. Liu S, Xing G, Liu J. Computational screening of single-atom catalysts for direct electrochemical NH3 synthesis from NO on defective boron phosphide monolayer. Appl Surf Sci 2023;611:155764.
21. Wang Q, Yu G, Yang E, Chen W. Through the self-optimization process to achieve high OER activity of SAC catalysts within the framework of TMO3@G and TMO4@G: a high-throughput theoretical study. J Colloid Interface Sci 2023;640:405-14.
22. Guo W, Wang S, Wang H, Cai Q, Zhao J. Cooperation between single atom catalyst and support to promote nitrogen electroreduction to ammonia: a theoretical insight. J Energy Chem 2024;96:336-44.
23. Yin Z, Li Y, Ye Y, et al. Sp/sp2 carbon ratio-driven high-throughput screening of electrocatalytic nitrogen reduction performance on transition metal single-atom catalysts. Rare Met 2024;43:5781-91.
24. Zhang Y, Wang X, Liu T, et al. Charge and spin communication between dual metal single-atom sites on C2N sheets: regulating electronic spin moments of Fe atoms for N2 activation and reduction. J Mater Chem A 2022;10:23704-11.
25. Liu J, Cai Y, Song R, et al. Recent progress on single-atom catalysts for CO2 electroreduction. Mater Today 2021;48:95-114.
26. Iqbal S, Safdar B, Hussain I, Zhang K, Chatzichristodoulou C. Trends and prospects of bulk and single-atom catalysts for the oxygen evolution reaction. Adv Energy Mater 2023;13:2203913.
27. Song W, Xiao C, Ding J, et al. Review of carbon support coordination environments for single metal atom electrocatalysts (SACS). Adv Mater 2024;36:e2301477.
28. Shen Y, Liang L, Zhang S, et al. Organelle-targeting surface-enhanced raman scattering (SERS) nanosensors for subcellular pH sensing. Nanoscale 2018;10:1622-30.
29. Liu S, Liu J. Rational design of highly efficient electrocatalytic single-atom catalysts for nitrogen reduction on nitrogen-doped graphene and g-C2N supports. J Power Sources 2022;535:231449.
30. Jiao D, Liu Y, Cai Q, Zhao J. Coordination tunes the activity and selectivity of the nitrogen reduction reaction on single-atom iron catalysts: a computational study. J Mater Chem A 2021;9:1240-51.
31. Long X, Huang F, Yao Z, et al. Advancements in electrocatalytic nitrogen reduction: a comprehensive review of single-atom catalysts for sustainable ammonia synthesis. Small 2024;20:e2400551.
32. Dai E, An W, Guo R, et al. Two-dimensional C2N-based single-atom catalyst with complex microenvironment for enhanced electrochemical nitrogen reduction: a descriptor-based design. J Energy Chem 2024;97:110-9.
33. Xu M, Ji Y, Qin Y, Dong H, Li Y. A universal descriptor for two-dimensional carbon nitride-based single-atom electrocatalysts towards the nitrogen reduction reaction. J Mater Chem A 2024;12:28046-55.
34. Liu Y, Zhao Z, Wei W, et al. Single-atom Fe-N4 on a carbon substrate for nitrogen reduction reaction. ACS Appl Nano Mater 2021;4:13001-9.
35. Shen P, Li X, Luo Y, Zhang N, Zhao X, Chu K. Ultra-efficient N2 electroreduction achieved over a rhodium single-atom catalyst (Rh1/MnO2) in water-in-salt electrolyte. Appl Catal B: Environ 2022;316:121651.
36. Feng X, Liu J, Chen L, et al. Hydrogen radical-induced electrocatalytic N2 reduction at a low potential. J Am Chem Soc 2023;145:10259-67.
37. Geng Z, Liu Y, Kong X, et al. Achieving a Record-High Yield Rate of 120.9 
for N2 electrochemical reduction over Ru single-atom Catalysts. Adv Mater 2018;30:e1803498.
38. Ling C, Ouyang Y, Li Q, et al. A general two-step strategy-based high-throughput screening of single atom catalysts for nitrogen fixation. Small Methods 2019;3:1800376.
39. Choi CH, Kim M, Kwon HC, et al. Tuning selectivity of electrochemical reactions by atomically dispersed platinum catalyst. Nat Commun 2016;7:10922.
40. Liu C, Cui Y, Zhou Y. The recent progress of single-atom catalysts on amorphous substrates for electrocatalysis. Energy Mater 2024;4:400067.
41. Yang X, Wan J, Zhang H, Wang Y. In situ modification of the d-band in the core-shell structure for efficient hydrogen storage via electrocatalytic N2 fixation. Chem Sci 2022;13:11030-7.
42. Liu X, Qi L, Song E, Gao W. Effective descriptor for nitrogen reduction on atomic catalysts. Catal Lett 2023;153:300-10.
43. Cui X, Wang X, Fu C, et al. Tuning the local coordination environment of silver(I) coordination networks with counterions for enhanced electrocatalytic CO2 reduction. Sci China Chem 2024;67:1524-30.
44. Sun Y, Fan W, Li Y, et al. Tuning coordination structures of Zn sites through symmetry-breaking accelerates electrocatalysis. Adv Mater 2024;36:e2306687.
45. Liu W, Liu S, Guo K, et al. Low-dimensional N-heterocyclic carbenes nanomaterials: promising supports of single atom catalysts. Vacuum 2024;227:113390.
46. Liu W, Xie Y, Tong Z, Sun J, Chen L, Xu J. Heterogeneous N-heterocyclic carbenes: efficient and selective metal-free electrocatalysts for CO reduction to multi-carbon products. J CO2 Util 2023;75:102566.
47. Sun J, Zheng D, Deng F, et al. Heterogeneous N-heterocyclic carbenes supported single-atom catalysts for nitrogen fixation: a combined density functional theory and machine learning study. Appl Surf Sci 2024;644:158802.
48. Liu W, Xie Y, Peng J, Zheng D, Lu W, Xu J. Synergistic N-heterocyclic carbene and C2N integration for efficient and selective metal-free photocatalytic CO reduction to C2H5OH. Appl Surf Sci 2024;678:161122.
49. Liu W, Sun J, Xie Y, Chen L, Xu J. The effective regulation of heterogeneous N-heterocyclic carbenes: structures, electronic properties and transition metal adsorption. Phys Chem Chem Phys 2023;25:28382-92.
50. Miller TS, Jorge AB, Suter TM, Sella A, Corà F, McMillan PF. Carbon nitrides: synthesis and characterization of a new class of functional materials. Phys Chem Chem Phys 2017;19:15613-38.
51. Longuinhos R, Ribeiro-soares J. Stable holey two-dimensional C2N structures with tunable electronic structure. Phys Rev B 2018:97.
52. Wei Z, He J, Yang Y, Xia Z, Feng Y, Ma J. Fe, V-co-doped C2N for electrocatalytic N2-to-NH3 conversion. J Energy Chem 2021;53:303-8.
53. Li Y, Yang T, Wang S, Bian Z, Liu Z. A recyclable Cu@C2N nano-catalyst applied in the transformation of alkynes: pH switchable access to ketones and 1,3-diynes. Green Chem 2024;26:2540-5.
54. Kresse G, Furthmüller J. Efficiency of ab-initio total energy calculations for metals and semiconductors using a plane-wave basis set. Comput Mater Sci 1996;6:15-50.
56. Hammer B, Hansen LB, Nørskov JK. Improved adsorption energetics within density-functional theory using revised perdew-burke-ernzerhof functionals. Phys Rev B 1999;59:7413-21.
58. Bussi G, Donadio D, Parrinello M. Canonical sampling through velocity rescaling. J Chem Phys 2007;126:014101.
59. 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;132:154104.
60. Bader RFW. A quantum theory of molecular structure and its applications. Chem Rev 1991;91:893-928.
61. Sahour H, Gholami V, Torkaman J, Vazifedan M, Saeedi S. Random forest and extreme gradient boosting algorithms for streamflow modeling using vessel features and tree-rings. Environ Earth Sci 2021;80:10054.
62. Umer M, Umer S, Zafari M, et al. Machine learning assisted high-throughput screening of transition metal single atom based superb hydrogen evolution electrocatalysts. J Mater Chem A 2022;10:6679-89.
63. Tamtaji M, Gao H, Hossain MD, et al. Machine learning for design principles for single atom catalysts towards electrochemical reactions. J Mater Chem A 2022;10:15309-31.
64. Ding R, Wang R, Ding Y, et al. Designing AI-aided analysis and prediction models for nonprecious metal electrocatalyst-based proton-exchange membrane fuel cells. Angew Chem Int Ed Engl 2020;59:19175-83.
65. Naceur H, Abdo HG, Igmoullan B, Namous M, Alshehri F, A Albanai J. Implementation of random forest, adaptive boosting, and gradient boosting decision trees algorithms for gully erosion susceptibility mapping using remote sensing and GIS. Environ Earth Sci 2024;83:11424.
66. Ali Y, Awwad E, Al-razgan M, Maarouf A. Hyperparameter search for machine learning algorithms for optimizing the computational complexity. Processes 2023;11:349.
67. Bischl B, Binder M, Lang M, et al. Hyperparameter optimization: Foundations, algorithms, best practices, and open challenges. WIREs Data Min Knowl 2023;13:e1484.
68. Pedregosa F, Varoquaux G, Gramfort A, et al. Scikit-learn: machine learning in python. J Mach Learn Res 2011;12:2825-30. Available from: https://dl.acm.org/doi/10.5555/1953048.2078195. [Last accessed on 28 Dec 2024].
69. Guyon I, Elisseeff A. An introduction to variable and feature selection. J Mach Learn Res 2003;3:1157-82.
70. Aas K, Jullum M, Løland A. Explaining individual predictions when features are dependent: more accurate approximations to shapley values. Artif Intell 2021;298:103502.
71. Lv X, Wei W, Huang B, Dai Y, Frauenheim T. High-throughput screening of synergistic transition metal dual-atom catalysts for efficient nitrogen fixation. Nano Lett 2021;21:1871-8.
72. Xiao Y, Shen C, Long T. Theoretical establishment and screening of an efficient catalyst for N2 electroreduction on two-dimensional transition-metal borides (MBenes). Chem Mater 2021;33:4023-34.
73. Seh ZW, Kibsgaard J, Dickens CF, Chorkendorff I, Nørskov JK, Jaramillo TF. Combining theory and experiment in electrocatalysis: Insights into materials design. Science 2017;355:eaad4998.
74. Lv SY, Huang CX, Li G, Yang LM. Electrocatalytic mechanism of N2 reduction reaction by single-atom catalyst rectangular TM-TCNQ monolayers. ACS Appl Mater Interfaces 2021;13:29641-53.
75. Liu L, Yang L, Zhang H, et al. Screening strategies for diatom metal-doped β-borophene nanosheet catalysts for electrochemical synthesis of ammonia using density functional theory. Energy Fuels 2023;37:9682-91.
76. Yue Y, Chen Y, Zhang X, Qin J, Zhang X, Liu R. High-throughput screening of highly active and selective single-atom catalysts for ammonia synthesis on WB2 (001) surface. Appl Surf Sci 2022;606:154935.
77. Pei W, Zhang W, Yu X, et al. Computational design of spatially confined triatomic catalysts for nitrogen reduction reaction. J Mater Inf 2023;3:26.
78. NIST Chemistry WebBook. NIST Standard Reference Database Number 69. Available from: https://doi.org/10.18434/T4D303. [Last accessed on 22 Dec 2024].
