Database of 2D hybrid perovskite materials: open-access collection of crystal structures, band gaps and atomic partial charges predicted by machine learning

TL;DR

This paper introduces an open-access database of 2D hybrid perovskite structures, along with machine learning models for predicting band gaps and atomic charges, aiding the design of new materials.

Contribution

It provides the first comprehensive database of 2D hybrid perovskites with structural data and develops ML models for property prediction, facilitating materials discovery.

Findings

Machine learning models predict band gaps within 0.1 eV accuracy.

Predicted band gaps decrease with increasing inorganic layers and bond angles.

The database reveals structure-property relationships in 2D perovskites.

Abstract

We describe a first open-access database of experimentally investigated hybrid organic-inorganic materials with 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, Sb) and 3 halogens (I, Br, Cl) known so far and will be regularly updated. The database contains a geometrical and crystal chemical analysis of the structures, which are useful to reveal quantitative structure-property relationships for this class of compounds. We show that the penetration depth of 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 the 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.