Active learning and explainable machine intelligence for lead-free chalcogenide perovskite photovoltaics: Gaussian process Bayesian optimization and SHAP interpretability applied to CaZrSe 3 /CdZnS solar cells

The global transition toward lead-free photovoltaics has intensified interest in chalcogenide perovskites with the formula ABX 3 , where calcium zirconium triselenide (CaZrSe 3 ) stands out for its direct bandgap of approximately 1.35 eV, chemical stability under ambient conditions, and complete absence of toxic elements. Yet the device physics of CaZrSe 3 heterojunction solar cells remain poorly mapped, and the conventional approach of exhaustive parametric sweeps in SCAPS-1D, while rigorous, places a prohibitive computational demand on research groups seeking rapid, systematic optimization. This work addresses this bottleneck by integrating a Gaussian process (GP) surrogate model, guided by the expected improvement acquisition function, into the SCAPS-1D simulation workflow for CaZrSe 3 /CdZnS heterojunction solar cells. A Latin hypercube initial sample of 403 simulations conditioned the GP; 201 sequential Bayesian optimization iterations then identified the global power conversion efficiency (PCE) optimum of 10.4927%, with V oc = 0.5388 V, J sc = 27.43 mA cm − 2 , and FF = 70.99%, using 604 total evaluations, representing a 95.0% reduction relative to the full 12 100-point reference grid. Concurrently, an ensemble of random forest, XGBoost, and artificial neural network surrogates trained on the complete 12 100-point dataset achieves R 2 > 0.997 for all performance targets, and SHapley Additive exPlanations analysis decomposes the PCE response surface to reveal that absorber acceptor doping ( N A ) is the primary determinant of device performance, while second-order interaction values quantify the coupled recombination dynamics of N A and ETL donor density N D . The optimal device specification an absorber thickness of 0.70 μ m, N A = 5.00 × 10