Page 109 - Read Online

Wu et al. J. Mater. Inf. 2025, 5, 15 https://dx.doi.org/10.20517/jmi.2024.67 Page 13 of 15

31. Willatt, M. J.; Musil, F.; Ceriotti, M. Feature optimization for atomistic machine learning yields a data-driven construction of the
periodic table of the elements. Phys. Chem. Chem. Phys. 2018, 20, 29661-8. DOI PubMed
32. Jäger, M. O. J.; Morooka, E. V.; Federici, C. F.; Himanen, L.; Foster, A. S. Machine learning hydrogen adsorption on nanoclusters
through structural descriptors. npj. Comput. Mater. 2018, 4, 96. DOI
33. De, S.; Bartók, A. P.; Csányi, G.; Ceriotti, M. Comparing molecules and solids across structural and alchemical space. Phys. Chem.
Chem. Phys. 2016, 18, 13754-69. DOI PubMed
34. Mai, H.; Le, T. C.; Chen, D.; Winkler, D. A.; Caruso, R. A. Machine learning for electrocatalyst and photocatalyst design and
discovery. Chem. Rev. 2022, 122, 13478-515. DOI
35. Nørskov, J. K.; Rossmeisl, J.; Logadottir, A.; et al. Origin of the overpotential for oxygen reduction at a fuel-cell cathode. J. Phys.
Chem. B. 2004, 108, 17886-92. DOI
36. Motagamwala, A. H.; Ball, M. R.; Dumesic, J. A. Microkinetic analysis and scaling relations for catalyst design. Annu. Rev. Chem.
Biomol. Eng. 2018, 9, 413-50. DOI PubMed
37. Pérez-Ramírez, J.; López, N. Strategies to break linear scaling relationships. Nat. Catal. 2019, 2, 971-6. DOI
38. Batchelor, T. A.; Pedersen, J. K.; Winther, S. H.; Castelli, I. E.; Jacobsen, K. W.; Rossmeisl, J. High-entropy alloys as a discovery
platform for electrocatalysis. Joule 2019, 3, 834-45. DOI
39. Artyushkova, K.; Pylypenko, S.; Olson, T. S.; Fulghum, J. E.; Atanassov, P. Predictive modeling of electrocatalyst structure based on
structure-to-property correlations of x-ray photoelectron spectroscopic and electrochemical measurements. Langmuir 2008, 24, 9082-
8. DOI PubMed
40. Hearst, M.; Dumais, S.; Osuna, E.; Platt, J.; Scholkopf, B. Support vector machines. IEEE. Intell. Syst. Their. Appl. 1998, 13, 18-28.
DOI
41. Sun, H.; Li, Y.; Gao, L.; et al. High throughput screening of single atomic catalysts with optimized local structures for the
electrochemical oxygen reduction by machine learning. J. Energy. Chem. 2023, 81, 349-57. DOI
42. Arjmandi, M.; Fattahi, M.; Motevassel, M.; Rezaveisi, H. Evaluating algorithms of decision tree, support vector machine and
regression for anode side catalyst data in proton exchange membrane water electrolysis. Sci. Rep. 2023, 13, 20309. DOI PubMed
PMC
43. Hossain, S. S.; Ali, S. S.; Rushd, S.; Ayodele, B. V.; Cheng, C. K. Interaction effect of process parameters and Pd-electrocatalyst in
formic acid electro-oxidation for fuel cell applications: implementing supervised machine learning algorithms. Int. J. Energy. Res.
2022, 46, 21583-97. DOI
44. Tamtaji, M.; Chen, S.; Hu, Z.; Goddard, I. W. A.; 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, 9992-10000. DOI
45. Anbari, E.; Adib, H.; Iranshahi, D. Experimental investigation and development of a SVM model for hydrogenation reaction of
carbon monoxide in presence of Co–Mo/Al O catalyst. Chem. Eng. J. 2015, 276, 213-21. DOI
2 3
46. Sun, J.; Chen, A.; Guan, J.; et al. Interpretable machine learning-assisted high-throughput screening for understanding NRR
electrocatalyst performance modulation between active center and C-N coordination. Energy. Environ. Mater. 2024, 7, e12693. DOI
47. Tan, S.; Wang, R.; Song, G.; et al. Machine learning and Shapley Additive Explanation-based interpretable prediction of the
electrocatalytic performance of N-doped carbon materials. Fuel 2024, 355, 129469. DOI
48. Zhang, Y.; Wang, Y.; Ma, N.; Fan, J. Directly predicting N electroreduction reaction free energy using interpretable machine
2
learning with non-DFT calculated features. J. Energy. Chem. 2024, 97, 139-48. DOI
49. Wei, C.; Shi, D.; Yang, Z.; et al. Data-driven design of double-atom catalysts with high H evolution activity/CO reduction
2
2
selectivity based on simple features. J. Mater. Chem. A. 2023, 11, 18168-78. DOI
50. Ying, Y.; Fan, K.; Luo, X.; Qiao, J.; Huang, H. Unravelling the origin of bifunctional OER/ORR activity for single-atom catalysts
supported on C N by DFT and machine learning. J. Mater. Chem. A. 2021, 9, 16860-7. DOI
2
51. Lin, S.; Xu, H.; Wang, Y.; Zeng, X. C.; Chen, Z. Directly predicting limiting potentials from easily obtainable physical properties of
graphene-supported single-atom electrocatalysts by machine learning. J. Mater. Chem. A. 2020, 8, 5663-70. DOI
52. Lu, S.; Song, P.; Jia, Z.; et al. Symbolic transform optimized convolutional neural network model for high-performance prediction
and analysis of MXenes hydrogen evolution reaction catalysts. Int. J. Hydrogen. Energy. 2024, 85, 200-9. DOI
53. Roy, D.; Charan, M. S.; Das, A.; Pathak, B. Unravelling CO reduction reaction intermediates on high entropy alloy catalysts: an
2
interpretable machine learning approach to establish scaling relations. Chemistry 2024, 30, e202302679. DOI PubMed
54. Wang, Y.; Zhang, Y.; Ma, N.; et al. Machine learning accelerated catalysts design for CO reduction: an interpretability and
transferability analysis. J. Mater. Sci. Technol. 2025, 213, 14-23. DOI
55. Jia, X.; Li, H. Machine learning enabled exploration of multicomponent metal oxides for catalyzing oxygen reduction in alkaline
media. J. Mater. Chem. A. 2024, 12, 12487-500. DOI
56. Yang, H.; Zhao, J.; Wang, Q.; et al. Convolutional neural networks and volcano plots: screening and prediction of two-dimensional
single-atom catalystsar. arXiv 2024, arXiv:2402.03876. Available online: https://doi.org/10.48550/arXiv.2402.03876. (accessed 15
Jan 2025)
57. Gilmer, J.; Schoenholz, S. S.; Riley, P. F.; Vinyals, O.; Dahl, G. E. Neural message passing for quantum chemistry. arXiv 2017,
arXiv:1704.01212. Available online: https://doi.org/10.48550/arXiv.1704.01212. (accessed 15 Jan 2025)
58. 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