(419f) Self-Driving Fluidic Labs: Autonomous Experimentation in Flow to Accelerate Materials and Molecular Discovery

Accelerating the discovery of new molecules and materials, as well as green and sustainable ways to synthesize and manufacture them, will have a profound impact on the global challenges in energy, sustainability, and healthcare. The current human-dependent paradigm of experimental research in chemical and materials sciences fails to identify technological solutions for worldwide challenges in a short timeframe. This limitation necessitates the development and implementation of new strategies to accelerate the pace of discovery. Recent advances in reaction miniaturization, automated experimentation, and data science provide an exciting opportunity to reshape the discovery and manufacturing 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 manufacturing of emerging advanced functional materials and molecules, with multi-step chemistries, through integration of flow chemistry, online characterization, and machine learning (ML). I will discuss how modularization of different chemical synthesis and processing stages in tandem with a constantly evolving ML modeling and decision-making under uncertainty can enable a resource-efficient navigation through high dimensional experimental design spaces (>10²⁰ possible experimental conditions). Example applications of SDFL for the autonomous synthesis of clean energy nanomaterials 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. Finally, I will present the unique reconfigurability aspect of flow chemistry to close the scale gap in chemical and materials research through facile switching from the reaction exploration/exploitation to smart manufacturing mode.