Page 77 - Read Online
P. 77
Page 14 of 15 Wu et al. J. Mater. Inf. 2025, 5, 14 https://dx.doi.org/10.20517/jmi.2024.77
19. Xiong, Y.; Wang, Y.; Zhou, J.; Liu, F.; Hao, F.; Fan, Z. Electrochemical nitrate reduction: ammonia synthesis and the beyond. Adv.
Mater. 2024, 36, e2304021. DOI PubMed
20. Zheng, S.; Yang, X.; Shi, Z. Z.; Ding, H.; Pan, F.; Li, J. F. The loss of interfacial water-adsorbate hydrogen bond connectivity position
surface-active hydrogen as a crucial intermediate to enhance nitrate reduction reaction. J. Am. Chem. Soc. 2024, 146, 26965-74. DOI
PubMed
21. Enger, B. C.; Holmen, A. Nickel and Fischer-Tropsch synthesis. Catal. Rev. 2012, 54, 437-88. DOI
22. Chen, Y.; Wei, J.; Duyar, M. S.; Ordomsky, V. V.; Khodakov, A. Y.; Liu, J. Carbon-based catalysts for Fischer-Tropsch synthesis.
Chem. Soc. Rev. 2021, 50, 2337-66. DOI PubMed
23. Weststrate, C. J. K.; Sharma, D.; Garcia, R. D.; Gleeson, M. A.; Fredriksson, H. O. A.; Niemantsverdriet, J. W. H. Mechanistic insight
into carbon–carbon bond formation on cobalt under simulated Fischer-Tropsch synthesis conditions. Nat. Commun. 2020, 11, 750.
DOI PubMed PMC
24. Ai, C.; Chang, J. H.; Tygesen, A. S.; Vegge, T.; Hansen, H. A. Impact of hydrogen concentration for CO2 reduction on PdHx : a
combination study of cluster expansion and kinetics analysis. J. Catal. 2023, 428, 115188. DOI
25. Deshpande, S.; Maxson, T.; Greeley, J. Graph theory approach to determine configurations of multidentate and high coverage
adsorbates for heterogeneous catalysis. npj. Comput. Mater. 2020, 6, 345. DOI
26. Bang, K.; Hong, D.; Park, Y.; Kim, D.; Han, S. S.; Lee, H. M. Machine learning-enabled exploration of the electrochemical stability of
real-scale metallic nanoparticles. Nat. Commun. 2023, 14, 3004. DOI PubMed PMC
27. Liu, P.; Wang, J.; Avargues, N.; et al. Combining machine learning and many-body calculations: coverage-dependent adsorption of
CO on Rh(111). Phys. Rev. Lett. 2023, 130, 078001. DOI PubMed
28. Jenner, B.; Köbler, J.; Mckenzie, P.; Torán, J. Completeness results for graph isomorphism. J. Comput. Syst. Sci. 2003, 66, 549-66.
DOI
29. Sumaria, V.; Sautet, P. CO organization at ambient pressure on stepped Pt surfaces: first principles modeling accelerated by neural
networks. Chem. Sci. 2021, 12, 15543-55. DOI PubMed PMC
30. Yang, Y.; Jiménez-Negrón, O. A.; Kitchin, J. R. Machine-learning accelerated geometry optimization in molecular simulation. J.
Chem. Phys. 2021, 154, 234704. DOI PubMed
31. Jha, D.; Gupta, V.; Ward, L.; et al. Enabling deeper learning on big data for materials informatics applications. Sci. Rep. 2021, 11,
4244. DOI PubMed PMC
32. Zhang, L.; Han, J.; Wang, H.; Car, R.; E, W. Deep potential molecular dynamics: a scalable model with the accuracy of quantum
mechanics. Phys. Rev. Lett. 2018, 120, 143001. DOI PubMed
33. 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 CO reduction. ACS. Catal. 2017, 7, 6600-8. DOI
2
34. Sumaria, V.; Nguyen, L.; Tao, F. F.; Sautet, P. Atomic-scale mechanism of platinum catalyst restructuring under a pressure of reactant
gas. J. Am. Chem. Soc. 2023, 145, 392-401. DOI PubMed
35. Xu, W.; Reuter, K.; Andersen, M. Predicting binding motifs of complex adsorbates using machine learning with a physics-inspired
graph representation. Nat. Comput. Sci. 2022, 2, 443-50. DOI PubMed
36. Boes, J. R.; Mamun, O.; Winther, K.; Bligaard, T. Graph theory approach to high-throughput surface adsorption structure generation.
J. Phys. Chem. A. 2019, 123, 2281-5. DOI PubMed
37. Eren, B.; Zherebetskyy, D.; Patera, L. L.; et al. Activation of Cu(111) surface by decomposition into nanoclusters driven by CO
adsorption. Science 2016, 351, 475-8. DOI PubMed
38. Pérez-Gallent, E.; Figueiredo, M. C.; Calle-Vallejo, F.; Koper, M. T. Spectroscopic observation of a hydrogenated CO dimer
intermediate during CO reduction on Cu(100) electrodes. Angew. Chem. Int. Ed. Engl. 2017, 56, 3621-4. DOI PubMed
39. Nakano, H.; Nakamura, I.; Fujitani, T.; Nakamura, J. Structure-dependent kinetics for synthesis and decomposition of formate species
over Cu(111) and Cu(110) model catalysts. J. Phys. Chem. B. 2001, 105, 1355-65. DOI
40. Hori, Y.; Takahashi, I.; Koga, O.; Hoshi, N. Electrochemical reduction of carbon dioxide at various series of copper single crystal
electrodes. J. Mol. Catal. A. Chem. 2003, 199, 39-47. DOI
41. Wang, S.; Jian, M.; Su, H.; Li, W. First-Principles microkinetic study of methanol synthesis on Cu(221) and ZnCu(221) surfaces. Chin.
J. Chem. Phys. 2018, 31, 284-90. DOI
42. Schouten, K. J. P.; Pérez, G. E.; Koper, M. T. M. Structure sensitivity of the electrochemical reduction of carbon monoxide on copper
single crystals. ACS. Catal. 2013, 3, 1292-5. DOI
43. Guo, W.; Vlachos, D. G. Effect of local metal microstructure on adsorption on bimetallic surfaces: atomic nitrogen on Ni/Pt(111). J.
Chem. Phys. 2013, 138, 174702. DOI PubMed
44. Deshpande, S.; Greeley, J. First-Principles analysis of coverage, ensemble, and solvation effects on selectivity trends in NO
electroreduction on Pt Sn alloys. ACS. Catal. 2020, 10, 9320-7. DOI
3
45. Ojeda, M.; Nabar, R.; Nilekar, A. U.; Ishikawa, A.; Mavrikakis, M.; Iglesia, E. CO activation pathways and the mechanism of Fischer-
Tropsch synthesis. J. Catal. 2010, 272, 287-97. DOI
46. Stróż, K. Plane groups - from basic to advanced crystallographic concepts. Z. Kristallogr. Cryst. Mater. 2003, 218, 642-9. DOI
47. Hoffmann, F. Symmetry in the plane: about wallpaper patterns, islamic mosaics, drawings from escher, and heterogeneous catalysts. In
Introduction to Crystallography; Hoffmann, F., Eds.; Springer International Publishing: Cham, 2020; pp 127-50. DOI
48. Hahn, T. The 17 plane groups (two-dimensional space groups). In International Tables for Crystallography Volume A: Space-group

