catena-Poly[tetrakis(3-phenylpropylammonium) [iodoplumbate(II)-tri-μ-iodo-plumbate(II)-tri-μ-iodo-iodoplumbate(II)-di-μ-iodo]]

Predictive Design Model for Low-Dimensional Organic-Inorganic Halide Perovskites Assisted by Machine Learning

Low-dimensional organic-inorganic halide perovskites have attracted interest for their properties in exciton dynamics, broad-band emission, magnetic spin selectivity. However, there is no quantitative model for predicting the structure-directing effect of organic cations on the dimensionality of these low-dimensional perovskites. Here, we report a machine learning (ML)-assisted approach to predict the dimensionality of lead iodide-based perovskites. A literature review reveals 86 reported amines that are classified into "2D"-forming and "non-2D"-forming based on the dimensionality of their perovskites. Machining learning models were trained and tested based on the classification and descriptor features of these ammonium cations. Four structural features, including steric effect index, eccentricity, largest ring size, and hydrogen-bond donor, have been identified as the key controlling factors. On the basis of these features, a quantified equation is created to calculate the probability of forming 2D perovskite for a selected amine. To further illustrate its predicting capability, the built model is applied to several untested amines, and the predicted dimensionality is verified by growing single crystals of perovskites from these amines. This work represents a step toward predicting the crystal structures of low dimensional hybrid halide perovskites using ML as a tool.

The classification of 1D `perovskites'

There has been a huge amount of interest in perovskites recently and new structures of hybrid perovskites are frequently reported. The classification of perovskites has been unambiguous in the discussion of 3D and layered 2D perovskites due to the dimensional constraints. However, in 1D perovskites, the additional degrees of freedom have resulted in a large number of possible structural configurations. The new proposed notation aims to classify these structures based on the connectivity of the octahedra of the perovskite, which has a periodic repeating pattern. However, the notation should be restricted to simple 1D perovskites and haloplumbate structures as the notation would become too cumbersome when applied to an exotic framework which has 3D characteristics, such as perovskite polytypes.

Hydrogen bonds and steric effects induced structural modulation of three layered iodoplumbate hybrids from nonperovskite to perovskite structure

Directed by diprotonated organic diamines containing both primary and tertiary ammonium groups, three layered iodoplumbate hybrids, {[H2DMPDA][PbI4]}n (1), {[H2DEPDA]4[Pb5I18]}n (2) and {[H2TMEDA][Pb3I8]}n (3) (DMPDA = N,N-dimethyl-1,3-propanediamine, DEPDA = N,N-diethyl-1,3-propanediamine, TMEDA = N,N,N',N'-tetramethylethylenediamine), have been synthesized solvothermally. 1 presents a layered perovskite structure based on corner-sharing PbI6 octahedra, compound 2 consists of a Pb4I20 perovskite motif and a Pb2I10 dimeric motif and compound 3 comprises Pb3I13 units connected by face-sharing and edge-sharing modes. Structural modulations from nonperovskite to perovskite structure are strongly correlated to steric effects and hydrogen bonding interaction at the organic-inorganic interface. Band gaps for 1-3 , estimated as 2.21, 2.58 and 2.73 eV, respectively, also reveal an interesting correlation with structural modulation, and the red shift for 1 is attributed to large Pb-I(equatorial)-Pb bond angles.