(209b) Accelerating the Discovery of Novel Perovskites with Robots and Data

In this talk, I will describe our Robotic-Accelerated Perovskite Investigation and Discovery (RAPID) system. The first generation of RAPID uses inverse temperature crystallization (ITC) to grow halide perovskite single crystals for x-ray structure determination and bulk characterization using commercial liquid handling robots. The second generation extends this to antisolvent vapor diffusion experiments. Experiment plans for the syntheses are contributed remotely, by both human scientists and algorithms trained on the reaction data, facilitating by our ESCALATE (Experiment Specification, Capture and Laboratory Automation Technology) data management system. Incoming data collected by ESCALATE is used to automatically train machine learning models, evaluate model performance and feature influence, and quantify reproducibility. A live web dashboard communicates these insights to the scientist and management in visual form. I will conclude by describing case studies about new scientific insights extracted from the comprehensive RAPID dataset that have been enabled by this comprehensive dataset, and discuss ongoing deployments of ESCALATE to perovskite thin film and vapor diffusion synthesis experiments.