References

najo

Nanosystems: Physics, Chemistry, Mathematics

Наносистемы: физика, химия, математика

2220-80542305-7971

Университет ИТМО

najo-1496

Research Article

CHEMISTRY AND MATERIALS SCIENCE

ХИМИЯ И НАУКА О МАТЕРИАЛАХ

Bridging accuracy and efficiency: assessing universal ML potentials for niobium-oxygen clusters

https://orcid.org/0000-0001-8312-7071

Popov

Ilya

popov@yandex.ru

https://orcid.org/0000-0003-1656-732X

Valeeva

Albina

anibla_v@mail.ru

https://orcid.org/0000-0001-6195-7971

Enyashin

Andrey

enyashin@ihim.uran.ru

Институт химии твердого тела УрО РАНInstitute of Solid State Chemistry, Ural Branch of Russian Academy of Sciences

Institute of Solid State Chemistry, Ural Branch of Russian Academy of SciencesRussian Federation

2025

05022026

165

1496

2025

Popov I., Valeeva A., Enyashin A.

Данная работа распространяется под лицензией Creative Commons Attribution 4.0.

This work is licensed under a Creative Commons Attribution 4.0 License.

https://nanojournal.ifmo.ru/jour/article/view/1496

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.

DFTMachine learning potentialMLIPEvolutionary algorithmNiobium oxideNbONanoparticleClusterUSPEX.

Russian Science Foundation (project 19-73-20012-П, https://rscf.ru/en/project/19-73-20012/)

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The authors declare that there are no conflicts of interest present.