Robot-Assisted Mapping of Precursor Formulation, Microstructure, and Electronic Structure in Lead-Free Silver–Bismuth-Iodide Materials for Photovoltaics

Silver–bismuth iodides offer a lead-free alternative for photovoltaic energy conversion, but their device performance depends strongly on the precursor formulation and the absorber formation. In this work, the AURORA robotic platform was adapted to investigate this formulation–performance landscape through sequential, population-level screening of 80 device-level records from mesoscopic photovoltaic devices. An initial concentration screen of an equimolar AgI:BiI3 formulation revealed a strongly nonmonotonic response, with the highest photovoltaic performance at a nominal loading of 1.0 mol L–1 for each precursor. Subsequent variation of the precursor ratio showed that the nominal stoichiometry alone is not sufficient to predict device performance. Under nominal 1000-lux white-LED illumination, the Ag-rich 2:1 formulation showed an apparent forward-scan power conversion efficiency of (19.8 ± 7.6)% (mean ± standard deviation, n = 16), calculated using a nominal incident irradiance of 0.25 mW cm–2, more than twice that of the equimolar formulation and more than five times that of the Bi-rich formulation. This improvement can predominantly be linked to the photocurrent rather than to open-circuit voltage and was associated with the formation of a more compact, interconnected absorber morphology. To determine whether atomic organization also constitutes an essential design variable, computations were performed on idealized Ag/Bi arrangements at the fixed AgBiI4 stoichiometry. The hybrid functional by Heyd, Scuseria and Ernzerhof (HSE06) was used for computations including spin–orbit coupling give a direct gap of 1.39 eV at Γ for the optimized layered model and an indirect Γ → R gap of 1.74 eV for the optimized three-dimensional model along the sampled path. The direct Γ gap of the three-dimensional model is 1.75 eV. Both models retain Ag–I-derived valence-band edges and Bi–I-derived conduction-band edges. These results identify precursor formulation and cation topology as two central design parameters in Ag–Bi–I photovoltaics: one governs the experimentally accessible processing window and device-level photocurrent, while the other tunes the intrinsic electronic structure without changing the nominal composition. More broadly, the current study demonstrates how robot-assisted experimentation and targeted electronic-structure computations can move photovoltaic optimization beyond nominal formulas toward processing-aware and microscopically informed materials design.