  Identification of crystal structures is a crucial stage in the exploration of
novel functional materials. This procedure is usually time-consuming and can be
false-positive or false-negative. This necessitates a significant level of
expert proficiency in the field of crystallography and, especially, requires
deep experience in perovskite - related structures of hybrid perovskites. Our
work is devoted to the machine learning classification of structure types of
hybrid lead halides based on available X-ray diffraction data. Here, we
proposed a simple approach to quickly identify of dimensionality of inorganic
substructures, types of lead halide polyhedra connectivity and structure types
using common powder XRD data and ML - decision tree classification model. The
average accuracy of our ML algorithm in predicting the dimensionality of
inorganic substructure, type of connection of lead halide and inorganic
substructure topology by theoretically calculated XRD pattern among 14 most
common structure types reaches 0.86+-0.05, 0.827+-0.028 and 0.71+-0.05,
respectively. The validation of our decision tree classification ML model on
experimental XRD data shows the accuracies of 1.0 and 0.82 for the dimension
and structure type prediction. Thus, our approach can significantly simplify
and accelerate the interpretation of highly complicated XRD data for hybrid
lead halides.
