Machine Learning for Understanding Compatibility of Organic-Inorganic Hybrid Perovskites with Post-Treatment Amines

Post-treatment is one of the facile and effective approaches to stabilize organic-inorganic hybrid perovskites. In this work, we apply a machine learning technique to study the trend of reactivity of different types of amines, which are used for the post-treatment of organic-inorganic hybrid perovskite films. Fifty amines are classified based on their compatibility with the methylammonium lead iodide films. Machine learning models are constructed from the classification of these amines and their molecular descriptor features. The model has achieved 86% accuracy on predicting the outcomes of whether perovskite films are maintained after post-treatment. By analyzing the constructed models, it was found that amines with fewer hydrogen bond donors and acceptors, more steric bulk, secondary, tertiary amines, and pyridine derivatives tend to have high compatibility with perovskite films.