Towards Self-Organizing Two-Dimensional Hybrid Perovskites—Machine Learning Driven Co-Navigation of Chemical and Compositional Spaces

Two-dimensional (2D) hybrid perovskites combine the richness of physical functionalities of inorganic materials and complexity and stimulus responsiveness of organic molecules in a single bulk dynamic material. The unique aspect of these materials is the thermodynamic (meta) stability, allowing for self-organized formation of complex large-period structures. Combined with the ease of fabrication, these materials not only have extensively demonstrated state-of-the-art high-performance optoelectronics but also offer the pathway towards versatile applications including sensors, electronic and neuromorphic devices as well as their cost-effective mass production. However, discovery and optimization of these materials require joint optimization of the composition of the inorganic components and selection of the molecular moieties, to harness the phase formation and self-assembly processes on the material level, and extend it to the micro- and macro scale functional devices. In this talk, I will discuss the potential of machine learning driven high throughput automated experiments to accelerate the discovery of 2D and quasi 2D hybrid perovskites, optimize the processing pathways, and understand the kinetics of their formation. I will also discuss how the high throughput liquid and solution-based synthesis can be transitioned into the thin film formation for functional applications.