REFERENCES

1. Masa, J.; Andronescu, C.; Schuhmann, W. Electrocatalysis as the nexus for sustainable renewable energy: the gordian knot of activity, stability, and selectivity. Angew. Chem. Int. Ed. Engl. 2020, 59, 15298-15312.

2. Ding, R.; Chen, J.; Chen, Y.; Liu, J.; Bando, Y.; Wang, X. Unlocking the potential: machine learning applications in electrocatalyst design for electrochemical hydrogen energy transformation. Chem. Soc. Rev. 2024, 53, 11390-11461.

3. Wang, D.; Cao, R.; Hao, S.; et al. Accelerated prediction of Cu-based single-atom alloy catalysts for CO₂ reduction by machine learning. Green. Energy. Environ. 2023, 8, 820-830.

4. Das, A.; Roy, D.; Manna, S.; Pathak, B. Harnessing the potential of machine learning to optimize the activity of Cu-based dual atom catalysts for CO₂ reduction reaction. ACS. Mater. Lett. 2024, 6, 5316-5324.

5. Abraham, B. M.; Piqué, O.; Khan, M. A.; Viñes, F.; Illas, F.; Singh, J. K. Machine learning-driven discovery of key descriptors for CO₂ activation over two-dimensional transition metal carbides and nitrides. ACS. Appl. Mater. Interfaces. 2023, 15, 30117-30126.

6. Tamtaji, M.; Kazemeini, M.; Abdi, J. DFT and machine learning studies on a multi-functional single-atom catalyst for enhanced oxygen and hydrogen evolution as well as CO₂ reduction reactions. Int. J. Hydrogen. Energy. 2024, 80, 1075-1083.

7. Chen, Z. W.; Lu, Z.; Chen, L. X.; Jiang, M.; Chen, D.; Singh, C. V. Machine-learning-accelerated discovery of single-atom catalysts based on bidirectional activation mechanism. Chem. Catal. 2021, 1, 183-195.

8. Huang, M.; Shi, R.; Liu, H.; et al. Computational single-atom catalyst database empowers the machine learning assisted design of high-performance catalysts. J. Phys. Chem. C. 2025, 129, 5043-5053.

9. Wei, C.; Shi, D.; Yang, Z.; et al. Data-driven design of double-atom catalysts with high H₂ evolution activity/CO₂ reduction selectivity based on simple features. J. Mater. Chem. A. 2023, 11, 18168-18178.

10. Zhao, Y.; Gao, S. S.; Ren, P. H.; Ma, L. S.; Chen, X. B. High-throughput screening and an interpretable machine learning model of single-atom hydrogen evolution catalysts with an asymmetric coordination environment constructed from heteroatom-doped graphdiyne. J. Mater. Chem. A. 2025, 13, 4186-4196.

11. Mou, L. H.; Du, J.; Li, Y.; Jiang, J.; Chen, L. Effective screening descriptors of metal-organic framework-supported single-atom catalysts for electrochemical CO₂ reduction reactions: a computational study. ACS. Catal. 2024, 14, 12947-12955.

12. Lin, X.; Du, X.; Wu, S.; et al. Machine learning-assisted dual-atom sites design with interpretable descriptors unifying electrocatalytic reactions. Nat. Commun. 2024, 15, 8169.

13. Sun, H.; Liu, J. y. Advancing CO₂RR with O-coordinated single-atom nanozymes: a DFT and machine learning exploration. ACS. Catal. 2024, 14, 14021-14030.

14. Batzner, S.; Musaelian, A.; Sun, L.; et al. E(3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials. Nat. Commun. 2022, 13, 2453.

15. Deng, B.; Zhong, P.; Jun, K.; et al. CHGNet as a pretrained universal neural network potential for charge-informed atomistic modelling. Nat. Mach. Intell. 2023, 5, 1031-1041.

16. Chen, C.; Ong, S. P. A universal graph deep learning interatomic potential for the periodic table. Nat. Comput. Sci. 2022, 2, 718-728.

17. Chen, C.; Ye, W.; Zuo, Y.; Zheng, C.; Ong, S. P. Graph networks as a universal machine learning framework for molecules and crystals. Chem. Mater. 2019, 31, 3564-3572.

18. Focassio, B. M. Freitas, L. P.; Schleder, G. R. Performance assessment of universal machine learning interatomic potentials: challenges and directions for materials’ surfaces. ACS. Appl. Mater. Interfaces. 2024, 17, 13111-13121.

19. Tan, C. W.; Descoteaux, M. L.; Kotak, M.; et al. High-performance training and inference for deep equivariant interatomic potentials. arXiv 2025, arXiv.2504.16068. Available online: https://doi.org/10.48550/arXiv.2504.16068 (accessed 28 October 2025).

20. Batatia, I.; Kovács, D. P.; Simm, G. N. C.; Ortner, C.; Csányi, G. MACE: higher order equivariant message passing neural networks for fast and accurate force fields. arXiv 2023, arXiv.2206.07697. Available online: https://doi.org/10.48550/arXiv.2206.07697 (accessed 28 October 2025).

21. Rhodes, B.; Vandenhaute, S.; Šimkus, V.; et al. Orb-v3: atomistic simulation at scale. arXiv 2025, arXiv.2504.06231. Available online: https://doi.org/10.48550/arXiv.2504.06231 (accessed 28 October 2025).

22. Zeng, J.; Zhang, D.; Peng, A.; et al. DeePMD-kit v3: a multiple-backend framework for machine learning potentials. J. Chem. Theory. Comput. 2025, 21, 4375-4385.

23. Fan, Z.; Zeng, Z.; Zhang, C.; et al. Neuroevolution machine learning potentials: Combining high accuracy and low cost in atomistic simulations and application to heat transport. Phys. Rev. B. 2021, 104, 104309.

24. Liu, J.; Yin, Q.; He, M.; Zhou, J. Constructing accurate machine-learned potentials and performing highly efficient atomistic simulations to predict structural and thermal properties. arXiv 2024, arXiv.2411.10911. Available online: https://doi.org/10.48550/arXiv.2411.10911 (accessed 28 October 2025).

25. Shiota, T.; Ishihara, K.; Do, T. M.; Mori, T.; Mizukami, W. Taming multi-domain, -fidelity data: towards foundation models for atomistic scale simulations. arXiv 2024, arXiv.2412.13088. Available online: https://doi.org/10.48550/arXiv.2412.13088 (accessed 28 October 2025).

26. Lim, Y.; Park, H.; Walsh, A.; Kim, J. Accelerating CO₂ direct air capture screening for metal-organic frameworks with a transferable machine learning force field. Matter 2025;8:102203.

27. Schaaf, L.; Fako, E.; De, S.; Schäfer, A.; Csányi, G. Accurate energy barriers for catalytic reaction pathways: an automatic training protocol for machine learning force fields. arXiv 2023, arXiv.2301.09931. Available online: https://doi.org/10.48550/arXiv.2301.09931 (accessed 28 October 2025).

28. Wu, S.; Zheng, S.; Zhang, W.; Zhang, M.; Li, S.; Pan, F. Machine-learning prediction of facet-dependent CO coverage on Cu electrocatalysts. J. Mater. Inf. 2025, 5, 14.

29. Wang, R.; Gao, Y.; Wu, H.; Zhong, Z. PFD: automatically generating machine learning force fields from universal models. arXiv 2025, arXiv.2502.20809. Available online: https://doi.org/10.48550/arXiv.2502.20809 (accessed 28 October 2025).

30. Jiao, Z.; Mao, Y.; Lu, R.; Liu, Y.; Guo, L.; Wang, Z. Fine-tuning graph neural networks via active learning: unlocking the potential of graph neural networks trained on nonaqueous systems for aqueous CO₂ reduction. J. Chem. Theory. Comput. 2025, 21, 3176-3186.

31. Zheng, B.; Oliveira, F. L.; Neumann Barros Ferreira, R.; et al. Quantum informed machine-learning potentials for molecular dynamics simulations of CO₂’s chemisorption and diffusion in Mg-MOF-74. ACS. Nano. 2023, 17, 5579-5587.

32. Voiry, D.; Chhowalla, M.; Gogotsi, Y.; et al. Best practices for reporting electrocatalytic performance of nanomaterials. ACS. Nano. 2018, 12, 9635-9638.

33. Fare, C.; Fenner, P.; Benatan, M.; Varsi, A.; Pyzer-knapp, E. O. A multi-fidelity machine learning approach to high throughput materials screening. npj. Comput. Mater. 2022, 8, 257.

34. Chanussot, L.; Das, A.; Goyal, S.; et al. Open Catalyst 2020 (OC20) dataset and community challenges. ACS. Catal. 2021, 11, 6059-6072.

35. Yin, J.; Li, W.; Chen, H.; et al. CaTS: toward scalable and efficient transition state screening for catalyst discovery. ACS. Catal. 2025, 15, 15754-15764.

36. Zhao, Y.; Li, Q. K.; Chi, C. L.; Gao, S. S.; Tang, S. L.; Chen, X. B. Design and screening of a NORR electrocatalyst with co-coordinating active centers of the support and coordination atoms: a machine learning descriptor for quantifying eigen properties. J. Mater. Chem. A. 2024, 12, 8226-8235.