Octahedral-motif-guided design of optoelectronic semiconductors via interpretable machine learning

Halide perovskites' remarkable optoelectronic properties stem from their metal halide octahedra. This connection underscores the promise of systematically extracting the physical rules encoded in octahedral motifs to steer the discovery of optoelectronic materials. Here, we develop an octahedral motif-centric, data-driven framework that couples interpretable machine learning with high-throughput first-principles calculations to accelerate the discovery of optoelectronic semiconductors. We construct motif-based descriptors to train a gradient boosting regression tree model for thermodynamic stability evaluation, achieving a low mean absolute error of 83 meV per atom on datasets comprising ~10⁴ materials. Leveraging the model to accelerate materials discovery, we identify 19 unexplored thermodynamically stable semiconductors with favorable optoelectronic properties. Among them Ca₂GaCoO₅ was successfully synthesized and experimentally verified to exhibit a strong visible light photoresponse. These results support the effectiveness of the machine learning framework for octahedra-containing semiconductors and suggest its potential for extension to other motif-based materials families.