Genome-Guided Interpretable Screening of Phase-Stable, Lead-Free Double Perovskite Absorbers for All-Inorganic Semiconductors, Sensors, and Photovoltaics with DFT-Validated Design Rules

Abstract

The discovery of stable, lead-free halide perovskites for optoelectronic applications is constrained by vast compositional space and limited interpretability of conventional screening approaches. We present a genome-guided, physics-informed framework that decodes thermodynamic stability and optoelectronic behavior through four physically interpretable descriptor families: packing, bonding, polarization, and electronic identity. Trained on 1,221 DFT-calculated A2BB'X6 compounds, machine-learning surrogates achieve robust predictive performance, with a recall-optimized stability classifier (ROC-AUC = 0.92) and an XGBoost regressor for band-gap prediction (R2 = 0.93 on held-out data). Applying a staged inverse-design constraint stack to 13,088 charge-balanced, lead-free compositions reduces the search space to five DFT-validated, phase-stable semiconductors: Rb2SnMnBr6, Cs2CdSnBr6, Cs2CdSnI6, Cs2KGaI6, and Cs2AgAlBr6. These candidates lie on the convex hull (E_hull <= 0 meV/atom), preserve ordered double-perovskite structures, and exhibit strong optical absorption (alpha peak ~1e5 cm^-1). Genotype-phenotype coupling analysis reveals a hierarchical control mechanism: packing genes define structural formability, bonding genes govern near-edge optical transitions and conductivity, and optoelectronic response genes regulate dielectric response and exciton screening (epsilon0 = 4.6-8.2). This work establishes a generalizable paradigm for interpretable inverse design, linking descriptor-level genomics to experimentally relevant optoelectronic phenotypes and providing design rules for discovering stable, lead-free double perovskites for photovoltaics, sensing, and transparent electronic applications.