Defect formation in CsSnI3 from Density Functional Theory and Machine Learning

作     者：Khamdang, Chadawan Wang, Mengen 

作者机构：Department of Electrical and Computer Engineering State University of New York at Binghamton BinghamtonNY13902 United States Materials Science and Engineering Program State University of New York at Binghamton BinghamtonNY13902 United States 

出 版 物：《arXiv》 (arXiv)

年 卷 期：2024年

核心收录：

主　　题：Perovskite 

摘      要：Sn-based perovskites as low-toxicity materials are actively studied for optoelectronic applications. However, their performance is limited by p-type self-doping, which can be suppressed by substitutional doping on the cation sites. In this study, we combine density functional theory (DFT) calculations with machine learning (ML) to develop a predictive model and identify the key descriptors affecting formation energy and charge transition levels of the substitutional dopants in CsSnI3. Our DFT calculations create a dataset of formation energies and charge transition levels and show that Y, Sc, Al, Zr, Nb, Ba, and Sr are effective dopants that pin the Fermi level higher in the band gap, suppressing the p-type self-doping. We explore ML algorithms and propose training a random forest regression model to predict the defect formation properties. This work shows the predictive capability of combining DFT with machine learning and provides insights into the important features that determine the defect formation energetics. © 2024, CC BY-NC-SA.