Background and Objective The recent years have seen the rapid emergency of a new class of solar cell based on mixed organic–inorganic halide perovskites. Incorporating perovskites into semiconductor devices such as solar cells has shown good performance. Although the first efficient solid-state perovskite cells were reported only in 2012, a very fast progress was made during five years with power conversion efficiencies reaching a confirmed 20%. There is a class of materials become idea candidate for quantum information processing and storage. However, there are many potential tunable parameters whose impact on properties of interest remains unknown. The objective of this project is to develop models that find relationships between the structures of hybrid organic perovskites (HOIPs) and the properties that are important for materials design, such as band gap, electron mobility, exciton lifetime, or the rate of spin transport. This project will incorporate a wide range of data, including published experimental data, simulated data from the Li research group, data from collaborators, as well as data from the Materials Project database. Additionally, the development of Python software framework will allow the integration of many types of data to be used for statistical inference and design, including electronic structure calculations, spectroscopic analysis, and molecular dynamics. The goal of our team is to enable the rational design of perovskite solar cells by optimizing tunable parameters to yield desired properties and behaviors.