  Low dimensional hybrid organic-inorganic perovskites (HOIPs) represent a
promising class of electronically active materials for both light absorption
and emission. The design space of HOIPs is extremely large, since a diverse
space of organic cations can be combined with different inorganic frameworks.
This immense design space allows for tunable electronic and mechanical
properties, but also necessitates the development of new tools for in silico
high throughput analysis of candidate structures. In this work, we present an
accurate, efficient, transferable and widely applicable machine learning
interatomic potential (MLIP) for predicting the structure of new 2D HOIPs.
Using the MACE architecture, an MLIP is trained on 86 diverse experimentally
reported HOIP structures. The model is tested on 73 unseen perovskite
compositions, and achieves chemical accuracy with respect to the reference
electronic structure method. Our model is then combined with a simple random
structure search algorithm to predict the structure of hypothetical HOIPs given
only the proposed composition. Success is demonstrated by correctly and
reliably recovering the crystal structure of a set of experimentally known 2D
perovskites. Such a random structure search is impossible with ab initio
methods due to the associated computational cost, but is relatively inexpensive
with the MACE potential. Finally, the procedure is used to predict the
structure formed by a new organic cation with no previously known corresponding
perovskite. Laboratory synthesis of the new hybrid perovskite confirms the
accuracy of our prediction. This capability, applied at scale, enables
efficient screening of thousands of combinations of organic cations and
inorganic layers.
