The first step before bromine doping within the device included validation studies of a pure tin-based system, MASnI 3: a power conversion efficiency (PCE) of 6.71% was achieved, having close congruence with experimental data. Five different algorithms were utilized to explore feature engineering. The devices were investigated through variations of bromine doping %, bandgap, electron affinity, series resistance, back-contact metal, and acceptor concentration─parameters that were specifically chosen because of their tunable nature and ability to be modified through facile experimental fabrication techniques of the device. Data-driven optimizations were carried out on 42 000 unique devices built utilizing a solar cell capacitance simulator (SCAPS). In this investigation, supervised machine learning (ML) was utilized to accurately predict the optimum bromine doping concentration in single-junction MASnI 3– xBr x devices.
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