(663a) Self-Driving Catalysis Lab: Research Acceleration in Homogeneous Catalysis Enabled By Autonomous Flow Reactors

Despite the widespread applications of transition metal-catalyzed homogeneous catalysis in manufacturing of fine/specialty chemicals, catalyst discovery and development are still based on Edisonian techniques. Existing material- and labor-intensive catalyst/ligand discovery strategies using batch reactors fall short in comprehensive exploration of the experimental space of homogeneous catalytic reactions due to the rapid growth of potential experiments over high dimensional experimental spaces. Thus, resulting in a slow and expensive catalyst/ligand discovery and development. Recent advances in flow chemistry, process intensification, and machine learning (ML)-assisted experimental planning^1,2 provide a unique opportunity to modernize and accelerate the catalyst/ligand discovery for transition metal-catalyzed reactions. In this talk, I will present an end-to-end 'Self-Driving Catalysis Lab' for autonomous ligand structure-yield-selectivity relationship mapping of transition metal-catalyzed reactions enabled by seamless integration of flow chemistry, robotics, and online characterization with ML. I will discuss how hardware modularization of the self-driving catalysis lab in tandem with a constantly evolving ML-assisted process modeling and decision-making under uncertainty can enable a resource-efficient navigation through a high dimensional experimental design space of organo-transition metal complexes.

References.

1 Abolhasani, M. & Kumacheva, E. The rise of self-driving labs in chemical and materials sciences. Nature Synthesis, doi:10.1038/s44160-022-00231-0 (2023).

2 Delgado-Licona, F. & Abolhasani, M. Research Acceleration in Self-Driving Labs: Technological Roadmap toward Accelerated Materials and Molecular Discovery. Advanced Intelligent Systems Advance Article, 2200331, doi:https://doi.org/10.1002/aisy.202200331 (2023).