Dataset Information

Machine Learning Approach to Delineate the Impact of Material Properties on Solar Cell Device Physics.

ABSTRACT: 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.

SUBMITTER: Islam MS

PROVIDER: S-EPMC9260917 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature

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Machine Learning Approach to Delineate the Impact of Material Properties on Solar Cell Device Physics.

Islam Md Shafiqul MS Islam Md Tohidul MT Sarker Saugata S Jame Hasan Al HA Nishat Sadiq Shahriyar SS Jani Md Rafsun MR Rauf Abrar A Ahsan Sumaiyatul S Shorowordi Kazi Md KM Efstathiadis Harry H Carbonara Joaquin J Ahmed Saquib S

ACS omega 20220622 26

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 ma ...[more]

PMID: 35811908

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Dataset Information

Machine Learning Approach to Delineate the Impact of Material Properties on Solar Cell Device Physics.

Publications

Machine Learning Approach to Delineate the Impact of Material Properties on Solar Cell Device Physics.

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