  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.
