Accelerated discovery of vanadium oxide compositions: A WGAN-VAE framework for materials design

The discovery of novel materials with tailored electronic properties is crucial for modern device technologies, but time-consuming empirical methods hamper progress. We present an inverse design framework combining an enhanced Wasserstein Generative Adversarial Network (WGAN) with a specialized Variational Autoencoder (VAE) to accelerate the discovery of stable vanadium oxide (V–O) compositions. Our approach features (1) a WGAN with integrated stability constraints and formation energy predictions, enabling direct generation of thermodynamically feasible structures, and (2) a refined VAE capturing atomic positions and lattice parameters while maintaining chemical validity. Applying this framework, we generated 451 unique V–O compositions, with 91 stable and 44 metastable under rigorous thermodynamic criteria. Notably, we uncovered several novel V${}_2$O${}_3$ configurations with formation energies below the Materials Project convex hull, revealing previously unknown stable phases. Detailed spin-polarized DFT+U calculations showed distinct electronic behaviors, including promising half-metallic characteristics. Our approach outperforms existing methods in both quality and stability, demonstrating about 20$\text{\%}$ stability rate under strict criteria compared to earlier benchmarks. Additionally, phonon calculations performed on selected compositions confirm dynamic stability: minor imaginary modes at 0 K likely stem from finite-size effects or known phase transitions, suggesting that these materials remain stable or metastable in practical conditions. These findings establish our framework as a powerful tool for accelerated materials discovery and highlight promising V–O candidates for next-generation electronic devices.