Database of Two-Dimensional Hybrid Perovskite Materials: Open-Access Collection of Crystal Structures, Band Gaps, and Atomic Partial Charges Predicted by Machine Learning

• Published in: Chemistry of materials Vol. 32; no. 17; pp. 7383 - 7388
• Main Authors: Marchenko, Ekaterina I, Fateev, Sergey A, Petrov, Andrey A, Korolev, Vadim V, Mitrofanov, Artem, Petrov, Andrey V, Goodilin, Eugene A, Tarasov, Alexey B
• Format: Journal Article
• Language: English
• Published: American Chemical Society 08-09-2020
• ISSN: 0897-4756; 1520-5002
• DOI: 10.1021/acs.chemmater.0c02290

We describe a first open-access database of experimentally investigated hybrid organic–inorganic materials with a two-dimensional (2D) perovskite-like crystal structure. The database includes 515 compounds, containing 180 different organic cations, 10 metals (Pb, Sn, Bi, Cd, Cu, Fe, Ge, Mn, Pd, and Sb) and 3 halogens (I, Br, and Cl) known so far and will be regularly updated. The database contains a geometrical and crystal chemical analysis of the structures, which are useful for revealing quantitative structure–property relationships for this class of compounds. We show that the penetration depth of the spacer organic cation into the inorganic layer and M–X–M bond angles increase in the number of inorganic layers (n). The machine learning model is developed and trained on the database for the prediction of a band gap with accuracy within 0.1 eV. Another machine learning model is trained for the prediction of atomic partial charges with accuracy within 0.01 e. We show that the predicted values of band gaps decrease with an increase of n and with an increase of M–X–M angles for single-layered perovskites. In general, the proposed database and machine learning models are shown to be useful tools for the rational design of new 2D hybrid perovskite materials.