Bridging accuracy and efficiency: assessing universal ML potentials for niobiumoxygen clusters
https://doi.org/10.17586/2220-8054-2025-16-5-619-627
Abstract
Machine Learning Interatomic Potentials (MLIPs) promise to combine the accuracy of DFT with the speed of classical force fields. However, their reliability for complex, multi-component systems requires rigorous validation. Here, we perform a targeted evaluation of three leading universal MLIPs using niobium oxide clusters (NbnOm, n ≤ 6, m ≤ 6) as a challenging test case. The Nb–O system is very well suited for this task due to its complex electronic interactions, manifested in existence of the bulk phase with 25% vacancy-ordered lattice and, at nanoscale, by a diverse range of non-stoichiometric clusters. We employ a dataset of global minima structures identified via DFT-based evolutionary search as a strict reference. A comparative analysis is then performed by executing evolutionary searches with the MLIPs. By directly comparing predicted structures, energies, and relative stability, we provide a comprehensive assessment of the accuracy and limitations of current universal potentials for modeling complex nanoscale oxides.
Keywords
DFT,
Machine learning potential,
MLIP,
Evolutionary algorithm,
Niobium oxide,
NbO,
Nanoparticle,
Cluster,
USPEX
Supporting agencies: This work was supported by the Russian Science Foundation (project 19-73-20012-Π, https://rscf.ru/en/project/19-73-20012/) and was performed at the Institute of Solid State Chemistry UB RAS.
About the Authors
I. S. Popov
Institute of Solid State Chemistry, Ural Branch of Russian Academy of Sciences
Russian Federation
Russian Federation
Ilya S. Popov
Yekaterinburg
A A. Valeeva
Institute of Solid State Chemistry, Ural Branch of Russian Academy of Sciences
Russian Federation
Russian Federation
Albina A. Valeeva
Yekaterinburg
A. N. Enyashin
Institute of Solid State Chemistry, Ural Branch of Russian Academy of Sciences
Russian Federation
Russian Federation
Andrey N. Enyashin
Yekaterinburg
References
1. Wan K., He J., Shi X. Construction of High Accuracy Machine Learning Interatomic Potential for Surface/Interface of Nanomaterials – A Review. Adv. Mater., 2024, 36(22), P. 2305758.
2. Wang G., Wang C., Zhang X., Li Z., Zhou J., Sun Z. Machine learning interatomic potential: Bridge the gap between small-scale models and realistic device-scale simulations. iScience, 2024, 27(5), P. 109673.
3. Mortazavi B., Zhuang X., Rabczuk T., Shapeev A.V. Atomistic modeling of the mechanical properties: the rise of machine learning interatomic potentials. Mater. Horiz., 2023, 10, P. 1956–1968.
4. Barroso-Luque L., Shuaibi M., Fu X., Wood B.M., Dzamba M., Gao M., Rizvi A., Zitnick C.L., Ulissi Z.W. Open Materials 2024 (OMat24) Inorganic Materials Dataset and Models. arXiv, 2024, [cond-mat.mtrl-sci], arXiv:2410.12771.
5. Ramlaoui A., Siron M., Djafar I., Musielewicz J., Rossello A., Schmidt V., Duval A. LeMat-Traj: A Scalable and Unified Dataset of Materials Trajectories for Atomistic Modeling. arXiv, 2025, [cs.LG], arXiv:2508.20875.
6. Jain A., Ong S.P., Hautier G., Chen W., Richards W.D., Dacek S., Cholia S., Gunter D., Skinner D., Ceder G., Persson K.A. Commentary: The Materials Project: A materials genome approach to accelerating materials innovation. APL Materials, 2013, 1, P. 011002.
7. Schmidt J., Cerqueira T.F.T., Romero A.H., Loew A., Jager F., Wang H.-C., Botti S., Marques M.A.L. Improving machine-learning models in ¨ materials science through large datasets. Materials Today Physics, 2024, 48, P. 101560.
8. Saal J.E., Kirklin S., Aykol M., Meredig B., Wolverton C. Materials Design and Discovery with High-Throughput Density Functional Theory: The Open Quantum Materials Database (OQMD). Jom., 2013, 65(11), P. 1501–1509.
9. Riebesell J., Goodall R.E.A., Benner P., Chiang Y., Deng B., Ceder G., Asta M., Lee A.A., Jain A., Persson K.A. A framework to evaluate machine learning crystal stability predictions. Nat. Mach. Intell., 2025, 7, P. 836–847.
10. Okatz A.M., Keesom P.H. Specific heat and magnetization of the superconducting monoxides: NbO and TiO. Phys. Rev. B, 1975, 12, P. 4917.
11. Bowman A.L., Wallace T.C., Yarnell J.L., Wenzel R.G. The crystal structure of niobium monoxide. Acta Crystallogr., 1966, 21, P. 843.
12. Kurmaev E.Z., Moewes A., Bureev O.G., Nekrasov I.A., Cherkashenko V.M., Korotin M.A., Ederer D.L. Electronic structure of niobium oxides. J. Alloys Compd., 2002, 347, P. 213–218.
13. Hu Z., Qian G., Li S., Yang L., Chen X., Weng M., Tan W., Pan F. Discovery of space aromaticity in transition–metal monoxide crystal Nb3O3 enabled by octahedral Nb6 structural units. Sci. Bull., 2019, 65, P. 367–372.
14. Miura A., Takei T., Kumada N., Wada S., Magome E., Moriyoshi C., Kuroiwa Y. Bonding Preference of Carbon, Nitrogen, and Oxygen in Niobium-Based Rock-Salt Structures. Inorg. Chem., 2013, 52, P. 9699.
15. Music D., Schmidt P., Mraz S. Adsorption of film-forming species on NbO and NbO ´ 2 surfaces. J. Vac. Sci. Technol. A., 2017, 35, P. 061512.
16. Fielicke A., Meijer G., Helden G. Infrared Spectroscopy of Niobium Oxide Cluster Cations in a Molecular Beam: Identifying the Cluster Structures. J. Am. Chem. Soc., 2003, 125, P. 3659–3667.
17. Deng H.T., Kerns K.P., Castleman A.W. Formation, Structures, and Reactivities of Niobium Oxide Cluster Ions. J. Phys. Chem., 1996, 100, P. 13386–13392.
18. Popov I.S., Valeeva A.A., Enyashin A.N. Identifying stable Nb–O clusters using evolutionary algorithm and DFT: A foundation for machine learning potentials. Chem. Phys., 2025, 590, P. 112533.
19. Lyakhov A.O., Oganov A.R., Stokes H., Zhu Q. New developments in evolutionary structure prediction algorithm USPEX. Comp. Phys. Comm., 2013, 184, P. 1172–1182.
20. Lepeshkin S.V., Baturin V.S., Uspenskii Y.A., Oganov A.R. Method for simultaneous prediction of atomic structure of nanoclusters in a wide area of compositions. J. Phys. Chem. Lett., 2019, 10, P. 102–106.
21. Heydariyan S., Nouri M.R., Alaei M., Allahyari Z., Niehaus T.A. New candidates for the global minimum of medium-sized silicon clusters: A hybrid DFTB/DFT genetic algorithm applied to Sin, n = 8–80. J. Chem. Phys., 2018, 149, P. 074313.
22. Olvera-Neria O., Garc´ıa-Cruz R., Gonzalez-Torres J., Garc´ıa-Cruz L.M., Castillo-Sanchez J.L., Poulain E. Strongly Bound Frenkel Excitons on ´ TiO2 Nanoparticles: An Evolutionary and DFT Approach. Int. J. Photoenergy, 2024, 2024, P. 4014216.
23. Yu X., Oganov A.R., Zhu Q., Qi F., Qian G. The stability and unexpected chemistry of oxide clusters. Phys. Chem. Chem. Phys., 2018, 20, P. 30437.
24. Mahdavifar Z. Prediction of unexpected BnPn structures: promising materials for non-linear optical devices and photocatalytic activities. Nanoscale Adv., 2021, 3, P. 2846.
25. Sandu M.P., Kovtunov M.A., Baturin V.S., Oganov A.R., Kurzina I.A. Influence of the Pd:Bi ratio on Pd–Bi/Al2O3 catalysts: structure, surface and activity in glucose oxidation. Phys. Chem. Chem. Phys., 2021, 23, P. 14889.
26. Zhou T., Ma L., Chen H. Electronic structure and stability of Al6CMn (M = Li, Na, K; n = 2, 4, 6) clusters. Comput. Theor. Chem., 2020, 1178, P. 112780.
27. Steshin I.S., Panteleev S.V., Petukhov I.V., Ignatov S.K. Parametrization of Gaussian approximation potential for the global optimization of magnesium clusters MgN (N≤100). Phys. Chem. Chem. Phys., 2025, 27, P. 18960–18977.
28. Kresse G., Furthmuller J. Efficient iterative schemes for ab initio total-energy calculations using a plane-wave basis set. ¨ Phys. Rev. B, 1996, 54, P. 11169.
29. Fu X., Wood B.M., Barroso-Luque L., Levine D.S., Gao M., Dzamba M., Zitnick C.L. Learning Smooth and Expressive Interatomic Potentials for Physical Property Prediction. arXiv, 2025, [physics.comp-ph], arXiv:2502.12147.
30. Rhodes B., Vandenhaute S., Simkus V., Gin J., Godwin J., Duignan T., Neumann M. Orb-v3: atomistic simulation at scale. ˇ arXiv, 2025, [condmat.mtrl-sci], arXiv:2504.06231.
31. Kim J., Kim J., Kim J., Lee J., Park Y., Kang Y., Han S. Data-efficient multi-fidelity training for high-fidelity machine learning interatomic potentials. arXiv, 2025, [cond-mat.mtrl-sci], arXiv:2504.06231.
32. Wood B.M., Dzamba M., Fu X., Gao M., Shuaibi M., Barroso-Luque L., Abdelmaqsoud K., Gharakhanyan V., Kitchin J.R., Levine D.S., Michel K., Sriram A., Cohen T., Das A., Rizvi A., Sahoo S.J., Ulissi Z.W., Zitnick C.L. UMA: A Family of Universal Models for Atoms. arXiv, 2025, [cs.LG], arXiv:2506.23971.
33. Larsen A.H., Mortensen J.J., Blomqvist J., Castelli I.E., Christensen R., Dułak M., Friis J., Groves M.N., Hammer B., Hargus C., Hermes E.D., Jennings P.C., Jensen P.B., Kermode J., Kitchin J.R., Kolsbjerg E.L., Kubal J., Kaasbjerg K., Lysgaard S., Maronsson J.B., Maxson T., Olsen T., Pastewka L., Peterson A., Rostgaard C., Schiøtz J., Schutt O., Strange M., Thygesen K.S., Vegge T., Vilhelmsen L., Walter M., Zeng Z., Jacobsen ¨ K.W. The atomic simulation environment – a Python library for working with atoms. J. Phys.: Condens. Matter, 2017, 29, P. 273002.
34. Thompson A.P., Aktulga H.M., Berger R., Bolintineanu D.S., Brown W.M., Crozier P.S., Veld P.J., Kohlmeyer A., Moore S.G., Nguyen T.D., Shan R., Stevens M.J., Tranchida J., Trott C., Plimpton S.J. LAMMPS – a flexible simulation tool for particle-based materials modeling at the atomic, meso, and continuum scales. Comp. Phys. Comm., 2022, 271, P. 10817.
35. Stukowski A. Visualization and analysis of atomistic simulation data with OVITO – the Open Visualization Tool. Modelling Simul. Mater. Sci. Eng., 2010, 18, P. 015012.
36. Hunter J.D. Matplotlib: A 2D Graphics Environment. Computing in Science & Engineering, 2007, 9(3), P. 90–95.
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For citations:
Popov I.S., Valeeva A.A., Enyashin A.N. Bridging accuracy and efficiency: assessing universal ML potentials for niobiumoxygen clusters. Nanosystems: Physics, Chemistry, Mathematics. 2025;16(5):619-627. https://doi.org/10.17586/2220-8054-2025-16-5-619-627
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