Machine Learning-Driven Analysis of Activation Energy for Metal Halide Perovskites
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Vimi patel , kunjrani sorathia , Kushal Unjiya , Raj patel , Siddhi Vinayak Pandey , Abul Kalam , Daniel Prochowicz , Seckin Akin and Pankaj Kumar Yadav
First published on 21st January 2025
Abstract
Metal halide perovskite single crystals (MHP SCs) are highly promising materials for optoelectronic applications, but their stability is hindered by ion migration, impacting performance. A key factor in understanding this issue is calculating the activation energy. Electrochemical Impedance Spectroscopy (EIS) is a powerful technique for separating ionic and electronic processes, yet traditional analysis is labour-intensive, involving extensive measurements, circuit fitting, and manual data interpretation. In this study, we introduce a machine learning (ML)-driven approach to fully automate EIS analysis. EIS data, measured for MAPbI₃ and MAPbBr₃ across temperatures from 263K to 343K, enabled the creation of a large database. The developed Machine-Learning (ML) model predicts EIS spectra at unknown temperatures, fits the appropriate electrical circuit, and automatically extracts passive component values to calculate activation energy via an Arrhenius plot. This automated workflow streamlines the calculation process, offering fast and reliable activation energy predictions even when temperature data is incomplete or missing. Our approach enhances the efficiency of EIS analysis, providing valuable insights into MHP SC stability and performance.