Unveiling the physical mechanisms underpinning bandgap variations in chalcopyrite crystals (ABX2) using interpretable artificial intelligence

Abstract

We propose an interpretable AI approach integrating hybrid DFT, symbolic regression, and data mining to predict chalcopyrite (ABX₂) bandgaps. Key factors, including atomic size, molar volume, and electron affinity, are identified, offering insights into bandgap-composition relationship and guiding high-performance materials design.