Exploration of the Stability of Multicomponent Metal Halide Perovskites utilizing Automated, High-throughput Methods and Machine Learning

Because of their outstanding optoelectronic properties and low-cost, solution-based fabrication, metal halide perovskites (MHP) are appealing candidates for a variety of applications, such as photovoltaics, light-emitting diodes, photodetectors, and ionizing radiation detectors. However, concerns of this material’s stability in pure or device-integrated form under external stimuli, such as light, humidity, oxygen, and heat, have prohibited the widespread utilizations of MHPs. It is well established that alloying can lessen detrimental effects of these factors. To date, a small portion of alloyed compositions have been investigated compared to the thousands of possible perovskites proposed theoretically. Conventional approaches to materials discovery and optimization, involving the modification of a single compositional or synthesis variable, observing the changes in functionalities, and making further improves, is incredibly inefficient in exploring vast design spaces. Therefore, there is an increasing necessity for an efficient workflow for the rapid synthesis, characterization, and exploration of MHPs via combinatorial synthesis in combination with high-throughput measurements and machine learning approaches.

Here, we develop this workflow that utilizes chemical robotics, rapid high-throughput PL measurements, and machine learning approaches, such as Gaussian process, to explore the intrinsic stability of four triple cation lead halide perovskite systems in Chapter 2. In Chapter 3, we demonstrate the universality of this workflow to explore the intrinsic stability of cesium lead halide quantum dots. As well, we incorporate another machine learning technique, Bayesian inference, to describe the changes in photoluminescent properties across the ternary compositional space. Next, we demonstrate how this workflow can also be utilized to explore experimental parameters in Chapter 4. There, we investigate how antisolvent engineering affects the intrinsic stability of binary MHP systems. Finally, we provide an outlook for future studies utilizing similar workflows in Chapter 5.