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

作者：Parvez Amin Khan, Muhammad Tipu Sultan, Md Mahamudul Islam, Md. Emran Hossain, Samiur Rahman · 发表于：Frontiers in Artificial Intelligence · 年份：2026 · DOI：10.3389/frai.2026.1882410 · 研究领域：Perovskite Materials and Applications、Machine Learning in Materials Science、solar cell performance optimization

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 oc , J sc , FF and PCE. Results The XGBoost surrogate achieves R 2 = 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...