Executive Summary : | The PI proposes the development of a self-driving autonomous materials synthesis and characterization lab using machine learning (ML) and robotics. This lab will offer unique opportunities for robust exploration of various parameters in materials synthesis and processing, with enhanced safety. The lab will tackle two problems: optimizing pylol synthesis and thin film deposition of Ag nanowire for high transmittance and low sheet resistance films, and investigating the effect of different parameters on drying of particle-laden sessile drops. The AI-driven lab will perform experiments with varying parameter conditions in an iterative and autonomous manner. The AI/ML algorithm will suggest the parameter conditions for the experimental trial, which is then executed by the robotic platform. Feedback from the trials is sent back to the AI/ML algorithm, which refines its model based on the new information and proposes the next set of parameter conditions for further process optimization. The first problem will lead to the development of Ag nanowires-based electronic devices with applications in solar cells, touch screen panels, and flat and flexible displays. The second problem will help determine experimental conditions for uniform deposition of carrier particles upon drop drying. The PI will share all experimental data from the lab in a database, allowing for detailed analysis of processing-structure-property relations in Ag nanowire synthesis and drop drying, and a better understanding of the underlying physical and chemical processes. |