Bandgap tuning strategy by cations and halide ions of lead halide perovskites learned from machine learning

Abstract

Bandgap engineering of lead halide perovskite materials is critical to achieve highly efficient and stable perovskite solar cells and color tunable stable perovskite light-emitting diodes.

Predicting the photon energy of quasi-2D lead halide perovskites from the precursor composition through machine learning

“…The machine-learning (ML) approach is a scientific model that can efficiently learn from existing results and is gaining increasing attention in material exploration. 18–22 With the assistance of ML, researchers can explore a large number of new materials (such as lead-free perovskites), 23,24 develop efficient solar cells, 19,21,22,27 etc. Previously, using ML algorithms, we successfully predicted the bandgap of 3D lead halide perovskites from their compositions and proposed possible compositions of mixed halide perovskites, which can be used in tandem solar cells.…”

“…Previously, using ML algorithms, we successfully predicted the bandgap of 3D lead halide perovskites from their compositions and proposed possible compositions of mixed halide perovskites, which can be used in tandem solar cells. 24 Marchenko et al established a database of 2D perovskites; they employed ML algorithms to predict the bandgap of 2D perovskites from their compositions, n values, crystal structures, and so on. 25 The following work by Wan et al utilized this database and ML algorithms to accurately predict the bandgap of 2D perovskite through molecular graphic descriptors.…”

Predicting Perovskite Bandgap and Solar Cell Performance with Machine Learning

“…[1] Hybrid perovskite is widely being used in a variety of applications, including solar cells, light-emitting diodes, lasers, and photodetectors, due to its longer electron and hole diffusion lengths and higher carrier mobility and a wide tunable bandgap (E g ). [1] The perovskite structure is represented by ABX 3 , [2,3] where A is an organic or inorganic cation, B is metal, and X is a halogen anion (Figure 1a). The rapid astonishing advances in the power conversion efficiency (PCE) of perovskite solar cells (PSCs) led to a rise in the PCE from 3.8% to 25.5% and currently occupy the center stage for photovoltaic research and development.…”

“…[ 12,13 ] The bandgap tuning and prediction are of significance for perovskite applications in light emission and harvesting. The bandgap of the perovskites can be varied from 1.5 to 3.2 eV with the anion selection, varying composition, and the A‐site cations (Cs, Rb formamidinium [FA], methylammonium [MA], [ 3 ] etc). A random forest (RF) model has been used to predict the bandgap of Li‐ and Na‐based perovskite using 18 physical descriptors, and 9328 types of materials with ideal bandgaps to capture solar light were estimated.…”

“…[ 14 ] Similarly, a linear regression model was developed to predict the bandgap of the mixed‐halide hybrid perovskite with higher accuracy (root mean squared error [RMSE] of 0.05 eV). [ 3 ] Zheng and co‐workers compared different ML models, that is, RF, ridge regression (RR), and support vector regression (SVR), to predict four target variables including perovskite bandgap from seven descriptors, and the authors noted the high accuracy of the RF model. [ 15 ] Compositional engineering of the perovskite is an effective approach to fabricate efficient PSCs.…”