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 . Treating the DFT results of the low‐ and intermediate‐temperature phases of 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 (‐) and (‐) 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 phase. These findings elucidate the structural and dynamical mechanisms underlying the structural properties of and highlight the utility of combining DFT and deep potential MD methods for studying complex phase transitions in transition metal oxides.