North Carolina State University | Rise of Self-Driving Labs in Chemical & Materials Sciences

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Milad Abolhasani, Associate Professor

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