REFERENCES
1. Meunier, F. C.; Cardenas, L.; Kaper, H.; et al. Synergy between metallic and oxidized Pt sites unravelled during room temperature CO oxidation on Pt/ceria. Angew. Chem. Int. Ed. Engl. 2021, 60, 3799-805.
2. Montini, T.; Melchionna, M.; Monai, M.; Fornasiero, P. Fundamentals and catalytic applications of CeO2-based materials. Chem. Rev. 2016, 116, 5987-6041.
3. Mudiyanselage, K.; Senanayake, S. D.; Feria, L.; et al. Importance of the metal-oxide interface in catalysis: in situ studies of the water-gas shift reaction by ambient-pressure X-ray photoelectron spectroscopy. Angew. Chem. Int. Ed. Engl. 2013, 52, 5101-5.
4. Rodriguez, J. A.; Grinter, D. C.; Liu, Z.; Palomino, R. M.; Senanayake, S. D. Ceria-based model catalysts: fundamental studies on the importance of the metal-ceria interface in CO oxidation, the water-gas shift, CO2 hydrogenation, and methane and alcohol reforming. Chem. Soc. Rev. 2017, 46, 1824-41.
5. Khivantsev, K.; Pham, H.; Engelhard, M. H.; et al. Transforming ceria into 2D clusters enhances catalytic activity. Nature 2025, 640, 947-53.
6. Shao, W.; Zhang, Y.; Zhou, Z.; et al. Dynamic control and quantification of active sites on ceria for CO activation and hydrogenation. Nat. Commun. 2024, 15, 9620.
7. Song, I.; Kovarik, L.; Engelhard, M. H.; Szanyi, J.; Wang, Y.; Khivantsev, K. Developing robust ceria-supported catalysts for catalytic NO reduction and CO/hydrocarbon oxidation. ACS. Catal. 2024, 14, 18247-55.
8. Fu, N.; Liang, X.; Wang, X.; et al. Controllable conversion of platinum nanoparticles to single atoms in Pt/CeO2 by laser ablation for efficient CO oxidation. J. Am. Chem. Soc. 2023, 145, 9540-7.
9. Li, Y.; Li, S.; Bäumer, M.; Ivanova-shor, E. A.; Moskaleva, L. V. What changes on the inverse catalyst? Insights from CO oxidation on Au-supported ceria nanoparticles using ab initio molecular dynamics. ACS. Catal. 2020, 10, 3164-74.
10. Liu, H.; Zhang, R.; Liu, S.; Liu, G. CeO2/Ni inverse catalyst as a highly active and stable Ru-free catalyst for ammonia decomposition. ACS. Catal. 2024, 14, 9927-39.
11. Esch, F.; Fabris, S.; Zhou, L.; et al. Electron localization determines defect formation on ceria substrates. Science 2005, 309, 752-5.
12. Paier, J.; Penschke, C.; Sauer, J. Oxygen defects and surface chemistry of ceria: quantum chemical studies compared to experiment. Chem. Rev. 2013, 113, 3949-85.
13. Wang, S.; Wu, Z.; Dai, S.; Jiang, D. E. Deep learning accelerated determination of hydride locations in metal nanoclusters. Angew. Chem. Int. Ed. Engl. 2021, 60, 12289-92.
14. Fang, C.; Wang, Z.; Guo, R.; Ding, Y.; Ma, S.; Sun, X. Machine learning potential for copper hydride clusters: a neutron diffraction-independent approach for locating hydrogen positions. J. Am. Chem. Soc. 2025, 147, 10750-7.
15. Telari, E.; Tinti, A.; Settem, M.; Maragliano, L.; Ferrando, R.; Giacomello, A. Charting nanocluster structures via convolutional neural networks. ACS. Nano. 2023, 17, 21287-96.
16. Boattini, E.; Dijkstra, M.; Filion, L. Unsupervised learning for local structure detection in colloidal systems. J. Chem. Phys. 2019, 151, 154901.
17. Zeni, C.; Rossi, K.; Pavloudis, T.; et al. Data-driven simulation and characterisation of gold nanoparticle melting. Nat. Commun. 2021, 12, 6056.
18. Mou, L.; Jiang, G.; Wang, C.; et al. Unveiling universal reactivity descriptors of metal clusters toward dinitrogen activation: a machine learning protocol with three-level feature extraction. ACS. Catal. 2025, 15, 6618-27.
19. Wang, Y.; Wang, C.; Mou, L. H.; Jiang, J. Deciphering experimental reactivity of metal clusters toward N2 activation using graph neural networks. JACS. Au. 2025, 5, 3669-78.
20. Zhao, X. G.; Yang, Q.; Xu, Y.; et al. Machine learning for experimental reactivity of a set of metal clusters toward C-H activation. J. Am. Chem. Soc. 2024, 146, 12485-95.
21. Lee, B.; Yoon, S.; Lee, J. W.; et al. Statistical characterization of the morphologies of nanoparticles through machine learning based electron microscopy image analysis. ACS. Nano. 2020, 14, 17125-33.
22. Rapetti, D.; Delle Piane, M.; Cioni, M.; Polino, D.; Ferrando, R.; Pavan, G. M. Machine learning of atomic dynamics and statistical surface identities in gold nanoparticles. Commun. Chem. 2023, 6, 143.
23. Banik, S.; Dutta, P. S.; Manna, S.; Sankaranarayanan, S. K. Development of a machine learning potential to study the structure and thermodynamics of nickel nanoclusters. J. Phys. Chem. A. 2024, 128, 10259-71.
24. Wang, X.; Shi, K.; Peng, A.; Snurr, R. Q. Computational chemistry and machine learning-assisted screening of supported amorphous metal oxide nanoclusters for methane activation. ACS. Catal. 2024, 14, 18708-21.
25. Zeng, F.; Cen, W. Machine-learning-accelerated structure prediction of PtSnO nanoclusters under working conditions. Phys. Chem. Chem. Phys. 2024, 26, 27624-32.
26. Nie, S.; Xiang, Y.; Wu, L.; et al. Active learning guided discovery of high entropy oxides featuring high H2-production. J. Am. Chem. Soc. 2024, 146, 29325-34.
27. Cai, H.; Ren, Q.; Gao, Y. Exploring the stable structures of cerium oxide nanoclusters using high-dimensional neural network potential. Nanoscale. Adv. 2024, 6, 2623-8.
28. Shi, J.; Ren, Q.; Gao, Y. Accelerating global optimization of cerium oxide nanocluster structures with high-dimensional neural network potential. J. Phys. Chem. A. 2025, 129, 2190-9.
29. Kresse, G.; Furthmüller, J. Efficient iterative schemes for ab initio total-energy calculations using a plane-wave basis set. Phys. Rev. B. Condens. Matter. 1996, 54, 11169-86.
30. Perdew, J. P.; Burke, K.; Ernzerhof, M. Generalized gradient approximation made simple. Phys. Rev. Lett. 1996, 77, 3865-8.
31. Dudarev, S. L.; Botton, G. A.; Savrasov, S. Y.; Humphreys, C. J.; Sutton, A. P. Electron-energy-loss spectra and the structural stability of nickel oxide: an LSDA+U study. Phys. Rev. B. 1998, 57, 1505-9.
32. Behler, J. Atom-centered symmetry functions for constructing high-dimensional neural network potentials. J. Chem. Phys. 2011, 134, 074106.
33. Behler, J.; Parrinello, M. Generalized neural-network representation of high-dimensional potential-energy surfaces. Phys. Rev. Lett. 2007, 98, 146401.
34. Singraber, A.; Morawietz, T.; Behler, J.; Dellago, C. Parallel multistream training of high-dimensional neural network potentials. J. Chem. Theory. Comput. 2019, 15, 3075-92.
35. Blank, T. B.; Brown, S. D. Adaptive, global, extended Kalman filters for training feedforward neural networks. J. Chemom. 2005, 8, 391-407.


