Multimodal Strategies for Novel Material Discovery and Enhanced Halide Perovskite Emitters

To meet the current and future global energy demands, we must implement a dual strategy of increased renewable energy conversion and reduced energy consumption. This will require us to substantially optimize current materials or discover and develop entirely new ones, for example, for solar cells and light-emitting diodes. A material with vast potential for these applications is nanocrystalline halide perovskite. However, one of the difficulties in improving perovskites is that the fabrication can be too fast to investigate with conventional approaches, and optimizing the resulting NCs can be an extremely tedious task. In this talk, I will discuss our new multimodal approaches to determine the structure and synthesis dynamics of highly confined 1D and 2D halide perovskite nanocrystals, tailor them to specific applications, and enhance their efficiency and stability.[1,2] I will also highlight our approach to incorporate a machine-learning process to optimize syntheses with minimal data demand. Importantly, many of these novel approaches can readily be applied to other systems, greatly benefitting material discovery and development.<br/> <br/>[1] C. Lampe, … , A. Gagliardi, A. S. Urban <i>Adv. Mater. </i><b>35</b>, 2208772 (<b>2023</b>)<br/>[2] S. Martin, … , A. S. Urban <i>Adv. Opt. Mater. </i><b>early view</b>, 2301009 (<b>2023</b>)