Page 78 - Read Online
P. 78
Wu et al. J. Mater. Inf. 2025, 5, 14 https://dx.doi.org/10.20517/jmi.2024.77 Page 15 of 15
symmetry; Hahn, T., Ed. Springer Netherlands: Dordrecht, 2002; pp 92-109. DOI
49. Chanussot, L.; Das, A.; Goyal, S.; et al. Open catalyst 2020 (OC20) dataset and community challenges. ACS. Catal. 2021, 11, 6059-72.
DOI
50. Lu, D.; Wang, H.; Chen, M.; et al. 86 PFLOPS deep potential molecular dynamics simulation of 100 million atoms with ab initio
accuracy. Comput. Phys. Commun. 2021, 259, 107624. DOI
51. Weng, M.; Wang, Z.; Qian, G.; et al. Identify crystal structures by a new paradigm based on graph theory for building materials big
data. Sci. China. Chem. 2019, 62, 982-6. DOI
52. Li, S.; Chen, Z.; Wang, Z.; et al. Graph-based discovery and analysis of atomic-scale one-dimensional materials. Natl. Sci. Rev. 2022,
9, nwac028. DOI PubMed PMC
53. Li, S.; Liu, Y.; Chen, D.; Jiang, Y.; Nie, Z.; Pan, F. Encoding the atomic structure for machine learning in materials science. WIREs.
Comput. Mol. Sci. 2022, 12, e1558. DOI
54. Xie, T.; Grossman, J. C. Crystal graph convolutional neural networks for an accurate and interpretable prediction of material
properties. Phys. Rev. Lett. 2018, 120, 145301. DOI PubMed
55. Steiner, M.; Reiher, M. Autonomous reaction network exploration in homogeneous and heterogeneous catalysis. Top. Catal. 2022, 65,
6-39. DOI PubMed PMC
56. Gu, G. H.; Lee, M.; Jung, Y.; Vlachos, D. G. Automated exploitation of the big configuration space of large adsorbates on transition
metals reveals chemistry feasibility. Nat. Commun. 2022, 13, 2087. DOI PubMed PMC
57. Qiao, Z.; Nie, W.; Vahdat, A.; Miller, T. F.; Anandkumar, A. State-specific protein-ligand complex structure prediction with a
multiscale deep generative model. Nat. Mach. Intell. 2024, 6, 195-208. DOI
58. Grabow, L. C.; Hvolbæk, B.; Nørskov, J. K. Understanding trends in catalytic activity: the effect of adsorbate-adsorbate interactions
for CO oxidation over transition metals. Top. Catal. 2010, 53, 298-310. DOI
59. Lausche, A. C.; Medford, A. J.; Khan, T. S.; et al. On the effect of coverage-dependent adsorbate-adsorbate interactions for CO
methanation on transition metal surfaces. J. Catal. 2013, 307, 275-82. DOI
60. Huang, Y.; Handoko, A. D.; Hirunsit, P.; Yeo, B. S. Electrochemical reduction of CO using copper single-crystal surfaces: effects of
2
*
CO coverage on the selective formation of ethylene. ACS. Catal. 2017, 7, 1749-56. DOI
61. Hou, J.; Chang, X.; Li, J.; Xu, B.; Lu, Q. Correlating CO coverage and CO electroreduction on Cu via high-pressure in situ
spectroscopic and reactivity investigations. J. Am. Chem. Soc. 2022, 144, 22202-11. DOI PubMed
62. Jin, J.; Wicks, J.; Min, Q.; et al. Constrained C adsorbate orientation enables CO-to-acetate electroreduction. Nature 2023, 617, 724-9.
2
DOI PubMed
63. Zheng, Y.; Zhang, J.; Ma, Z.; et al. Seeded growth of gold-copper janus nanostructures as a tandem catalyst for efficient
electroreduction of CO to C products. Small 2022, 18, e2201695. DOI PubMed
2 2+
64. Sun, W.; Wang, P.; Jiang, Y.; et al. V-doped Cu Se hierarchical nanotubes enabling flow-cell CO electroreduction to ethanol with
2 2
high efficiency and selectivity. Adv. Mater. 2022, 34, e2207691. DOI PubMed
65. Yan, X.; Chen, C.; Wu, Y.; et al. Boosting CO electroreduction to C products on fluorine-doped copper. Green. Chem. 2022, 24,
2 2+
1989-94. DOI
66. Reller, C.; Krause, R.; Volkova, E.; et al. Selective electroreduction of CO toward ethylene on nano dendritic copper catalysts at high
2
current density. Adv. Energy. Mater. 2017, 7, 1602114. DOI
*
67. Xiang, K.; Shen, F.; Fu, Y.; et al. Boosting CO electroreduction towards C products via CO intermediate manipulation on copper-
2+
2
based catalysts. Environ. Sci. Nano. 2022, 9, 911-53. DOI

