Non-technical Description: Hybrid Organic Inorganic Structures (HOIS), specifically in the form of metal-halide perovskites, have recently attracted much attention due to unprecedented performance advancements in solar cells, light emitting diodes, as well as emerging applications in transistors, sensors, spintronics and catalysts. The extremely wide chemical and structural space engendered by hybrid organic-inorganic systems presents both exciting opportunities for property tunability, but also substantial challenges associated with the laborious process of exploring this wide space for suitable structures for a given application. This project aims to strongly accelerate structure prediction within the HOIS space through exploitation of recently curated X-ray structure databases, molecular dynamics simulation, machine learning (ML), synthetic and structural studies in an iterative feedback loop. The research will provide critical insights into composition-structure relationships, including the preferred structural dimensionality, distortions in the inorganic lattice, relative stabilities of different perovskite-like structures, and the underlying molecular features. The outcome will be the rapid prediction of hybrid organic-inorganic perovskite-type structures from the starting materials, which is essential to optimize optical, electronic and spin properties for a wide range of applications. Approximately one thousand new HOIS will be explored, more than doubling the range o