Machine Learning Approach for Predicting the Hole Mobility of the Perovskite Solar Cells

Abstract

Traditional computational approaches such as Monte Carlo simulation, molecular dynamics, and density functional theory (DFT) have contributed to understanding the role of hole mobility in the development of suitable hole transporting materials (HTMs) for perovskite solar cell efficiency. However, these methods often involve significant computational expenses, thereby limiting the number of feasible studies and hindering the extraction of valuable structure−property guidelines for a rational design of novel HTMs. To address these challenges, this study proposes an ultrafast predictive model that balances prediction accuracy and time efficiency, a critical aspect for predicting the hole mobility of HTMs for perovskite optimization. The model, which leverages the Random Forest learning algorithm, enables comprehensive and rapid analysis of photovoltaic features taken from previous experiments/literature and processed with the RDKit Python library. Notably, recognizing the challenges associated with high correlation in the dataset, a method to improve the calculation of feature importance in random forests is applied. The results highlight bertz_ct, chi1, and logP as key predictive features of HTM performance. However, the model underscores the need to uncover additional predictive features based on structure–property relationships to predict the optimized hole mobility. This approach significantly accelerates the discovery process, outperforming prevalent statistical methods in the prediction of hole mobility and HTM performance. This research signifies a pivotal step toward cost‐effective, accelerated stability and efficiency of HTMs, with implications for the advancement of optoelectronic devices.