Supervised Machine Learning-Aided SCAPS-Based Quantitative Analysis for the Discovery of Optimum Bromine Doping in Methylammonium Tin-Based Perovskite (MASnI3- xBr x)

Hasan Al Jame; Saugata Sarker; Md Shafiqul Islam; Md Tohidul Islam; Abrar Rauf; Sumaiyatul Ahsan; Sadiq Shahriyar Nishat; Md Rafsun Jani; Kazi Md Shorowordi; Joaquin Carbonara; Saquib Ahmed

doi:10.1021/acsami.1c15030

Supervised Machine Learning-Aided SCAPS-Based Quantitative Analysis for the Discovery of Optimum Bromine Doping in Methylammonium Tin-Based Perovskite (MASnI_{3- x}Br _x)

ACS Appl Mater Interfaces. 2022 Jan 12;14(1):502-516. doi: 10.1021/acsami.1c15030. Epub 2021 Dec 28.

Affiliations

¹ Department of Materials and Metallurgical Engineering (MME), Bangladesh University of Engineering and Technology (BUET), East Campus, Dhaka 1000, Bangladesh.
² Department of Materials Design and Innovation, University at Buffalo, Buffalo, New York 14260, United States.
³ Department of Materials Science and Engineering (MSE), Rensselaer Polytechnic Institute, 110 8th street, Troy, New York 12180, United States.
⁴ Department of Mathematics, SUNY─Buffalo State, 1300 Elmwood Avenue, Buffalo, New York 14222, United States.
⁵ Department of Mechanical Engineering Technology, SUNY─Buffalo State, 1300 Elmwood Avenue, Buffalo, New York 14222, United States.

PMID: 34962754
DOI: 10.1021/acsami.1c15030

Abstract

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. Data-driven optimizations were carried out on 42 000 unique devices built utilizing a solar cell capacitance simulator (SCAPS). 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. Five different algorithms were utilized to explore feature engineering. The first step before bromine doping within the device included validation studies of a pure tin-based system, MASnI₃: a power conversion efficiency (PCE) of 6.71% was achieved, having close congruence with experimental data. ML analyses for optimal bromine doping resulted in the discovery of two devices with bromine concentrations of 22.43% (Br22) and 25.63% (Br25), with the latter being a more fine-tuned value obtained through extra rigorous analysis. To understand the total and relative impact of each feature on power conversion efficiency (PCE), Br22 and Br25 were analyzed with a state-of-the-art algorithm, namely, the SHapley Additive exPlanations (SHAP) algorithm. Focusing on the two discovered devices, further device optimizations were carried out utilizing SCAPS. Modulations of absorber thickness, bulk and interfacial defect density, and choice of electron transport layer (ETL) and hole transport layer (HTL) materials were tried. Device stability was analyzed through carrier lifetime studies. Following these optimization steps, Br22 and Br25 demonstrated final high PCE values of 20.72 and 17.37%, respectively. The ML-assisted quantitative analysis of the current work provides significant confidence for optimal bromine-doped tin-based devices to be considered as viable and competitive nontoxic alternatives to traditional technologies.

Keywords: Br doping; MASnI3; SCAPS; SHAP analysis; machine learning; single-junction perovskite.