In this research,
solar cell capacitance simulator-one-dimensional
(SCAPS-1D) software was used to build and probe nontoxic Cs-based
perovskite solar devices and investigate modulations of key material
parameters on ultimate power conversion efficiency (PCE). The input
material parameters of the absorber Cs-perovskite layer were incrementally
changed, and with the various resulting combinations, 63,500 unique
devices were formed and probed to produce device PCE. Versatile and
well-established machine learning algorithms were thereafter utilized
to train, test, and evaluate the output dataset with a focused goal
to delineate and rank the input material parameters for their impact
on ultimate device performance and PCE. The most impactful parameters
were then tuned to showcase unique ranges that would ultimately lead
to higher device PCE values. As a validation step, the predicted results
were confirmed against SCAPS simulated results as well, highlighting
high accuracy and low error metrics. Further optimization of intrinsic
material parameters was conducted through modulation of absorber layer
thickness, back contact metal, and bulk defect concentration, resulting
in an improvement in the PCE of the device from 13.29 to 16.68%. Overall,
the results from this investigation provide much-needed insight and
guidance for researchers at large, and experimentalists in particular,
toward fabricating commercially viable nontoxic inorganic perovskite
alternatives for the burgeoning solar industry.