Proceedings of International Conference on Perovskite Thin Film Photovoltaics and Perovskite Photonics and Optoelectronics (NIPHO24)
Publication date: 25th April 2024
The exceptional properties of metal halide perovskites make them ideal materials for photovoltaic applications, with the promise to completely disrupt the areas of building-based, utility scale, and space-based photovoltaic. However, the fundamental principles of microstructure formation, evolution, and stability that are crucial for designing functional perovskite devices are understood only weakly. Currently, this is the only remaining bottleneck for the lab-to-fab transformation and realization of the scalable manufacturing of these materials. In this talk, I will discuss the potential of machine learning-driven high throughput automated experiments to expedite the discovery of metal halide perovskites, optimize processing pathways, and enhance understanding of formation kinetics1-5. Additionally, I will showcase how high throughput automated synthesis provides a comprehensive guide for designing optimal precursor stoichiometry to achieve functional quasi-2D perovskite phases in films capable of realizing high-performance optoelectronics3,4. I further introduce the concept of co-navigation of theory and experiment spaces to accelerate the discovery and design of metal halide perovskites. These studies exemplify how a high-throughput automated experimental workflow effectively expedites discoveries and processing optimizations in complex materials systems with multiple functionalities, facilitating their realization in scalable optoelectronic manufacturing processes.