PerovskiteOpt-AI: A Machine Learning-Driven Multi-Parameter Optimization Framework for Lead-Free Perovskite Solar Cell Device Architecture Using SCAPS-1D Simulation and Gaussian Process Surrogate Modeling

The commercialization of perovskite solar cells (PSCs) hinges on replacing toxic lead-based absorbers with environmentally benign alternatives while maintaining competitive power conversion efficiencies (PCE). However, the enormous parameter space governing lead-free device architectures—spanning absorber thickness, defect density, doping concentration, and charge transport layer (CTL) selection—renders traditional trial-and-error optimization impractical. This paper introduces PerovskiteOpt-AI, a machine learning (ML)-driven multi-parameter optimization framework that integrates SCAPS-1D device simulation with Gaussian process (GP) surrogate modeling and Bayesian optimization (BO) to systematically identify high-efficiency lead-free PSC configurations. A synthetic dataset of 12,000 device-level simulations generated for the FTO/WS2/CsSnI3/CuSCN/Au architecture by varying eight critical parameters. An ensemble of ML models—random forest (RF), XGBoost, and GP regression (GPR)—is trained and benchmarked, with XGBoost achieving an R2 of 0.9987 and RMSE of 0.041% for PCE prediction. The GP surrogate is then coupled with a BO loop employing expected improvement (EI) acquisition to navigate the design space, converging on an optimized PCE of 27.83% ± 0.21% within 150 iterations—a 38.6% relative improvement over the baseline. Shapley additive explanations (SHAP) analysis reveals that absorber defect density and perovskite thickness are the dominant efficiency drivers, while conduction band offset at the ETL/absorber interface governs open-circuit voltage. The proposed framework reduces the computational cost of full-factorial parametric sweeps by over 95%, establishing a scalable paradigm for accelerated, interpretable design of next-generation lead-free consumer-grade photovoltaic devices.