Accelerated prediction of dielectric functions in solar cell materials with graph neural networks (2510.08738v1)

Abstract: We present an atomistic line graph neural network (ALIGNN) model for predicting dielectric functions directly from crystal structures. Trained on $\sim$7000 dielectric functions from the JARVIS-DFT database computed with a meta-GGA exchange-correlation functional, the model accurately reproduces spectral features, including peak intensities and overall line shapes, while enabling efficient high-throughput screening. Applied to the recently developed Alexandria materials database, containing over four hundred thousand insulating materials, we uncover a clear elemental trend, with vanadium emerging as a strong indicator of materials with high-spectroscopic limited maximum efficiency (SLME). In particular, vanadium-based perovskite materials show a substantially higher fraction of high-SLME compounds compared to the database average, underscoring their promise for optoelectronic applications.