Quantitative assessment of the structure of (Na2O)0.1-(V2O5)0.6-(TeO2)0.3 glass: An ab initio and machine learning interatomic potential study of oxide systems with mixed-valence

This study investigates the atomic-scale structure of a glassy system using first-principles molecular dynamics (FPMD) and machine-learned interatomic potentials (MLIP). The structural models generated using MLIP demonstrate excellent agreement with FPMD results while offering substantial computational efficiency, enabling the exploration of much larger system sizes that are prohibitive for FPMD while retaining comparable accuracy. Validation against experimental X-ray total structure factors and pair distribution functions indicates the reliability of both the FPMD- and MLIP-generated structures. The glass network is found to be composed of a mix of VO 4 , VO 5 and VO 6 polyhedra, TeO 3 , TeO 4 and TeO 5 units. Local environments are studied through maximally localized Wannier function (MLWF)-based bonding analysis, coordination number analysis, and bond angle distributions. The MLIP model is further used to study glass compositions with varying amounts of V 5 + and V 4 + to investigate oxidation-state-dependent structural changes. Our results demonstrate that melt-quench glass models produced through MLIP-based molecular dynamics and subsequently relaxed via a single-shot DFT calculation can capture changes in local topology, coordination, and V–O bonding motifs with high accuracy, revealing the reliability of the MLIP model in describing mixed-valence oxide glass systems. This finding can be explained by charge self-regulation in transition-metal oxides, where changes in formal oxidation state are accommodated by local metal–O electronic redistribution and accompanying structural rearrangements. This work highlights the capability of MLIPs to accurately describe complex oxide glasses with multicomponent compositions and variable oxidation states, extending their applicability to chemically diverse disordered systems.