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

Introduction: 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. Methods: 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 Results: The XGBoost surrogate achieves R Discussion: 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.