Supervised Machine Learning-Aided SCAPS-Based Quantitative Analysis for the Discovery of Optimum Bromine Doping in Methylammonium Tin-Based Perovskite (MASnI_3–xBr_x)

Abstract

In this investigation, supervised machine learning (ML) was utilized to accurately predict the optimum bromine doping concentration in single-junction MASnI 3−x Br 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, MASnI 3 : 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.

Rational design of formamidine tin-based perovskite solar cell with 30% potential efficiency via 1-D device simulation

“…The bulk and interface defect optimizations of the absorber are carried out through the modulation of the defect energy level and defect density. 53 Defects were considered to be ''Single'' in this simulation. The equations that describe defect properties are as follows.…”

Simple Structural Descriptor Obtained from Symbolic Classification for Predicting the Oxygen Vacancy Defect Formation of Perovskites

“…The confusion matrix is a 2 × 2 matrix used to visually represent the true and false positive and negative classes predicted by the model. From top left to bottom right is the number of true classes (11), false-negative classes (01), false-positive classes (10), and true-negative classes (00). It can be used to calculate metrics like accuracy, recall, and precision.…”

“…Such research focuses on applying existing machine learning algorithms to the energy field. For example, studies like finding high-conductivity photovoltaic materials, discovering rapid host materials in Li–S batteries, discovering the optimum bromine doping in methylammonium tin-based perovskites, researching the formation and thermal stability of perovskites, and deep mining stable and nontoxic hybrid organic–inorganic perovskites for photovoltaics, can guide researchers to understand the internal mechanism of materials and prepare new high-performance materials. Second, due to the complex preparation process of lithium batteries and solar cells, , the preparation of these devices by high-throughput machine learning and AI platforms to accelerate devices’ development, such as building an ensemble learning platform for the large-scale exploration of new double perovskites, predicting battery end of life from solar off-grid system field data, estimating the remaining charge of Li-ion batteries based on the noise immune state, and rapidly optimizing multiscale droplet generation by computer vision, are also the research focus.…”

Halide Composition Engineered a Non-Toxic Perovskite–Silicon Tandem Solar Cell with 30.7% Conversion Efficiency

“…Singh et al 47 reported MA tin iodide (MASnI 3 )-based PSC with copper oxide (Cu 2 O) as HTL and TiO 2 as ETL, which emulated PCE of 27.43%. Recently, Jame et al 48 used supervised machine learning for the MASnI 3−x Br x device to predict optimized concentrations of bromine doping and found PCE of 22.43 and 25.63% with bromine concentrations of Br 22 and Br 25 , respectively. Another recent work, reported by Xu et al 49 in the context of lead-free PSC, shows PCE of 6.8% using 2-thiopheneethylammonium (TEA + ) as a spacer cation for MASnI 3 (MA: CH 3 NH 3 + ).…”