Rise of Self-Driving Labs in Chemical & Materials Sciences: Accelerated Discovery and Manufacturing of Energy Materials

Despite the intriguing properties and widespread applications of semiconductor nanomaterials in energy technologies, their discovery, synthesis, and manufacturing are still based on Edisonian trial-and-error based techniques. Existing materials development strategies using resource-intensive batch reactors with irreproducible and uncontrollable heat/mass transport rates very often fail to overcome the demands of the vast synthesis and processing universe of energy materials, resulting in a slow and expensive discovery and development timeframe (8-10 years). Recent advances in lab automation and machine learning (ML)-guided modeling/decision-making strategies provide an exciting opportunity to reshape the discovery and manufacturing of emerging solution-processed energy materials.1 In this talk, I will present an end-to-end ‘self-driving lab’ for autonomous discovery, development, and on-demand manufacturing of energy materials through convergence of flow chemistry, robotics, and in-situ material characterization with ML.2 I will discuss how modularization of different stages of materials synthesis and processing in tandem with an ensemble neural network modeling and decision-making under uncertainty can enable a resource-efficient navigation through an experimentally accessible high dimensional space. An application of the self-driving lab for the autonomous synthesis of metal halide perovskite quantum dots will be presented