Role of Hydrogen Bonding on the Design of New Hybrid Perovskites Unraveled by Machine Learning.

Selecting a set of reactants to accurately design a new low dimensional hybrid perovskite could greatly accelerate the discovery of materials with great potential in photovoltaics, or solid-state lighting. However, this design is challenging as most hybrid metal halides are not perovskites and no feature is clearly associated to the structural characteristics of the inorganic metal halide network. This work first demonstrates that the organic molecules are key parameters to determine the structure type of the inorganic network (i.e., perovskite versus non-perovskite). Then, machine learning (ML) algorithms are used to identify the key features of the organic cations leading to the perovskite structure type. Using a large dataset of hybrid metal halides, this work extracts the organic molecules of all hybrid lead halide compounds, calculates 2756 molecular descriptors and fingerprints for each of these molecules, and are able to predict through ML techniques if a specific organic amine will lead to the perovskite type with an accuracy up to 88.65%. Descriptors related to hydrogen bonding are identified as important features. Thus, a simple but reliable design principle could be demonstrated: the presence of primary ammonium cation is the primary condition to prepare hybrid lead halide perovskites regardless of their dimensionalities.