Data-driven design of high-performance MASnxPb1-xI3 perovskite materials by machine learning and experimental realization

Abstract

The photovoltaic performance of perovskite solar cell is determined by multiple interrelated factors, such as perovskite compositions, electronic properties of each transport layer and fabrication parameters, which makes it rather challenging for optimization of device performances and discovery of underlying mechanisms. Here, we propose and realize a novel machine learning approach based on forward-reverse framework to establish the relationship between key parameters and photovoltaic performance in high-profile MASnxPb1-xI3 perovskite materials. The proposed method establishes the asymmetrically bowing relationship between band gap and Sn composition, which is precisely verified by our experiments. Based on the analysis of structural evolution and SHAP library, the rapid-change region and low-bandgap plateau region for small and large Sn composition are explained, respectively. By establishing the models for photovoltaic parameters of working photovoltaic devices, the deviation of short-circuit current and open-circuit voltage with band gap in defective-zone and low-bandgap-plateau regions from Shockley-Queisser theory is captured by our models, and the former is due to the deep-level traps formed by crystallographic distortion and the latter is due to the enhanced susceptibility by increased Sn4+ content. The more difficulty for hole extraction than electron is also concluded in the models and the prediction curve of power conversion efficiency is in a good agreement with Shockley-Queisser limit. With the help of search and optimization algorithms, an optimized Sn:Pb composition ratio near 0.6 is finally obtained for high-performance perovskite solar cells, then verified by our experiments. Our constructive method could also be applicable to other material optimization and efficient device development.

Revolutionizing Low‐Cost Solar Cells with Machine Learning: A Systematic Review of Optimization Techniques

“…Another case study presented by Xia et al [ 59 ] combined ML techniques with an efficient forward‐inverse method to research M A S n x P b 1 x I 3 material and explored high‐ performance PSCs. With 14 physicochemical parameters and the Sn–Pb ratio as inputs, the E g model of M A S n x P b 1 x I 3 was first developed for forward analysis, and the asymmetrically bowing relationship between the Sn–Pb ratio and the E g of OMHP was used.…”

Machine learning in energy chemistry: introduction, challenges and perspectives

“…They also gave a corresponding explanation based on XPS and FTIR results that the capping layer on top stabilizes 3 by changing the surface structure and chemistry, which match the previous experiment regulations 167,168 . Besides, by machine learning and experiment verification, Cai et al confirmed the ratio of Sn: Pb in MASn x Pb 1 -xI 3 holding an Sn-Pb alloy within the perovskite crystal 169 .…”

“…166,167 Besides, by machine learning and experimental verification, Cai et al confirmed the ratio of Sn: Pb in MASn x Pb 1 - x I 3 holding an Sn–Pb alloy within the perovskite crystal. 168…”

“…166,167 Besides, by machine learning and experimental verification, Cai et al confirmed the ratio of Sn: Pb in MASn x Pb 1 -xI 3 holding an Sn-Pb alloy within the perovskite crystal. 168 In exploring the device stability condition part, Hu et al investigated the factors affecting the stability of perovskite solar cell devices through a combination of experiments and machine learning. 169 Five factors affecting the efficiency and stability (grain size, defect density, bandgap, fluorescence lifetime and surface roughness) were selected using machine learning models and proposed that roughness and crystal size have a strong influence on long-term stability.…”

Lead-free perovskite-inspired semiconductors for indoor light-harvesting – the present and the future

“…In addition to the material screening for the photovoltaic applications, 1,100 the suitability of artificial intelligence in predicting the stability, and the reap, rest, and recovery (3R) of halide perovskites has been also reported and may applied also to PIM-based IPVs to boost their credibility for future practical applications. 101…”