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.