Finite-temperature properties of NbO₂ from a deep-learning interatomic potential

Abstract

Using first-principles-based machine-learning potential, molecular dynamics (MD) simulations are performed to investigate the micro-mechanism in phase transition of NbO₂. Treating the DFT results of the low- and intermediate-temperature phases of NbO₂ as training data in the deep-learning model, we successfully constructed an interatomic potential capable of accurately reproducing the phase transitions from low-temperature (pressure) to high-temperature (pressure) regimes. Notably, our simulations predict a high-pressure monoclinic phase (>14 GPa) without treating its information in the training set, consistent with previous experimental findings, demonstrating the reliability of the constructed interatomic potential. We identified the Nb-dimers as the key structural motif governing the phase transitions. At low temperatures, the displacements of the Nb-dimers drive the transition between the I4₁/a (α-NbO₂) and I4₁ (β-NbO₂) phases, while at high temperatures, Nb ions are prone to being equally distributed and the disappearance of Nb-dimers leads to the stabilization of a high-symmetry P4₂/mnm phase. These findings elucidate the structural and dynamical mechanisms underlying the structural properties of NbO₂ and highlight the utility of combining DFT and deep potential MD methods for studying complex phase transitions in transition metal oxides.

Keywords

deep-learning model / density functional theory / interatomic potential / molecular dynamics / phase transitions

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2025 The Author(s). Materials Genome Engineering Advances published by Wiley-VCH GmbH on behalf of University of Science and Technology Beijing.