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Minisymposium Presentation
Accelerated Materials and Molecular Discovery with Self-Driving Fluidic Labs
Presenter
Milad Abolhasani is an Associate Professor, ALCOA Scholar, and a University Faculty Scholar in the Department of Chemical and Biomolecular Engineering at North Carolina State University. He received his Ph.D. from the University of Toronto in 2014. Prior to joining NC State University, he was an NSERC Postdoctoral Fellow in the Department of Chemical Engineering at MIT (2014-2016). At NC State University, Dr. Abolhasani leads a diverse flow chemistry research group that studies self-driving labs tailored toward accelerated discovery, development, and manufacturing of advanced functional materials and molecules using autonomous experimentation. Dr. Abolhasani has received numerous awards and fellowships, including NSF CAREER Award, AIChE 35 Under 35, Dreyfus Award for Machine Learning in the Chemical Sciences & Engineering, AIChE NSEF Young Investigator Award, ALCOA Research Achievement Award, I &EC Research 2021 Class of Influential Researchers, AIChE Futures Scholar, and Emerging Investigator recognition from Lab on a Chip, Nanoscale, Reaction Chemistry & Engineering, and Journal of Flow Chemistry.
Description
Accelerating the discovery of new molecules and materials, as well as green and sustainable ways to synthesize and manufacture them, will profoundly impact the worldwide challenges in energy, sustainability, and healthcare. The current human-dependent paradigm of experimental research in chemical and materials sciences fails to identify technological solutions quickly. Recent advances in reaction miniaturization, automated experimentation, and data science provide an exciting opportunity to reshape the discovery and development of new molecules and materials related to energy transition and sustainability. In this talk, I will present a 'self-driving fluidic lab (SDFL)' for autonomous discovery and development of emerging advanced functional materials and molecules through integrating flow chemistry, online characterization, and machine learning (ML). I will discuss how modularization of different chemical synthesis and processing stages in tandem with constantly evolving ML modeling and decision-making under uncertainty can enable resource-efficient navigation through high dimensional experimental design spaces (>1020 possible experimental conditions). Example applications of the SDFL for the autonomous synthesis of quantum dots and specialty chemicals will be presented to illustrate the potential of autonomous robotic experimentation in reducing synthetic route discovery timeframe from >10 years to a few months.