Accelerated discovery of high-performance small-molecule hole transport materials via molecular splicing, high-throughput screening, and machine learning

As the most representative and widely utilized hole transport material (HTM), spiro-OMeTAD encounters challenges including limited hole mobility, high production costs, and demanding synthesis conditions. These issues have a notable impact on the overall performance of perovskite solar cells (PSCs) based on spiro-OMeTAD and hinder its large-scale commercial application. Consequently, there exists a strong demand for high-throughput computational design of novel small-molecule HTMs (SM-HTMs) that are cost-effective, easy to synthesize, and offer excellent performance. In this study, a systematic and iterative design and development process for SM-HTMs is proposed, aiming to accelerate the discovery and application of high-performance SM-HTMs. A custom-developed molecular splicing algorithm (MSA) generated a sample space of 200,000 intermediate molecules, culminating in the creation of a comprehensive database of over 7,000 potential SM-HTM candidates. In total, six promising HTM candidates were identified through MSA, density functional theory calculations and high-throughput screening. Furthermore, three machine learning algorithms, namely random forest, gradient boosting decision tree, and extreme gradient boosting (XGBoost), were employed to construct predictive models for key material properties, including hole recombination energy, solvation free energy, maximum absorption wavelength, and hydrophobicity. Among these, the XGBoost-based model demonstrated the best overall performance. The MSA methodology combining comprehensive SM-HTM database and performance prediction models, as introduced in this study, offers a powerful and universal toolkit for the design and optimization of next-generation SM-HTMs, thereby paving the way for future advancements of PSCs.