Deep generative modeling for AI-guided inverse design of perovskite photovoltaic devices

Perovskite solar cells (PSCs) have rapidly approached the performance ceiling of mature single-junction photovoltaics, yet further improvement is constrained by the high-dimensional, non-linear coupling between device parameters and power-conversion efficiency (PCE). This work presents an end-to-end AI-guided inverse-design framework that learns the conditional distribution of device parameters given target photovoltaic figures of merit.

The framework is trained and validated on 49,998 drift-diffusion simulations of PSCs balanced across three classes of dominant recombination mechanism. A physics-informed feature-engineering pipeline feeds an ensemble of forward surrogate models under a strictly leakage-controlled five-fold cross-validation protocol. A conditional variational autoencoder with feature-wise linear modulation (FiLM) and classifier-free guidance (CFG) generates device candidates conditioned on target V_oc, J_sc, FF and PCE.

The XGBoost surrogate achieves R² = 0.8661 ± 0.0020 on the PCE proxy, statistically outperforming five competitors (Wilcoxon p < 10^-190) while indistinguishable from LightGBM (p = 0.51). SHAP, permutation importance, and mutual-information converge on parasitic series resistance and grain-boundary defect density as dominant PCE-limiting parameters. At the optimal guidance scale (w = 1.5), cVAE+CFG achieves hit-rates of 73.7%, 12.6%, and 3.4% at the 90th-, 99th-percentile and "Ultra" targets-improvements of 8.9 ×, 25.2 ×, and ≥34 × over random sampling, with 100% valid/unique and ≥99.8% novel candidates.

Kolmogorov-Smirnov tests confirm generated devices preserve energy-level marginals while concentrating mass in the high-performance sub-manifold. The framework offers a transferable, reproducible, and statistically rigorous methodology for accelerating design of next-generation perovskite PV devices.