Precision Synthesis and Accelerated Optimization of Colloidal Quantum Dots with Self-Driving Fluidic Labs

Despite the intriguing physicochemical properties and widespread applications of colloidal quantum dots (QDs) in energy and chemical technologies, their discovery and synthesis optimization are still based on Edisonian techniques. Existing QD discovery and development strategies using batch reactors with irreproducible and uncontrollable heat/mass transport rates very often fail to comprehensively explore the vast synthesis and processing space of QDs. These limitations necessitate the development and implementation of new strategies to accelerate the pace of QD discovery and development. Recent advances in reaction miniaturization, automated experimentation, and data science provide an exciting opportunity to reshape the discovery, development, and manufacturing of QDs. In this talk, I will present a <i>Self-Driving Fluidic Lab</i> (SDFL) for accelerated discovery, optimization, and manufacturing of colloidal QDs with multi-step chemistries, through the integration of flow chemistry, online characterization, and machine learning (ML). ^1-5 I will discuss how modularization of different synthesis and processing stages in tandem with a constantly evolving ML-assisted QD synthesis modeling and decision-making under uncertainty can enable resource-efficient navigation through high dimensional experimental design spaces. Example applications of SDFLs for the autonomous precision synthesis of metal halide perovskite, II–VI, and III–V QDs will be presented to illustrate the potential of autonomous labs in reducing QD development timeframe from &gt;10 years to a few months. Finally, I will present the unique reconfigurability aspect of SDFLs to close the scale gap in nanomanufacturing research through on-demand switching from reaction exploration/exploitation to smart nanomanufacturing mode.<br/><br/><b>References.</b><br/>[1] Abolhasani, M.; Kumacheva, E. The rise of self-driving labs in chemical and materials sciences. <i>Nature Synthesis</i>, 2, 483–492, <b>2023</b><br/>[2] Volk, A. A.; Epps, R. W.; Yonemoto, D. T.; Masters, B. S.; Castellano, F. N.; Reyes, K. G.; Abolhasani, M. AlphaFlow: autonomous discovery and optimization of multi-step chemistry using a self-driven fluidic lab guided by reinforcement learning. <i>Nature Communications</i>, 14 (1), 1403, <b>2023</b>.<br/>[3] Volk, A. A.; Abolhasani, M. Autonomous flow reactors for discovery and invention. <i>Trends in Chemistry</i>, 3 (7), 519-522, <b>2021</b>.<br/>[4] Delgado-Licona, F.; Abolhasani, M. Research Acceleration in Self-Driving Labs: Technological Roadmap toward Accelerated Materials and Molecular Discovery. <i>Advanced Intelligent Systems</i>, Advance Article, 2200331, <b>2023</b>. https://doi.org/10.1002/aisy.202200331<br/>[5] Epps, R. W.; Bowen, M. S.; Volk, A. A.; Abdel-Latif, K.; Han, S.; Reyes, K. G.; Amassian, A.; Abolhasani, M. Artificial Chemist: An Autonomous Quantum Dot Synthesis Bot. <i>Advanced Materials</i>, 32 (30), 2001626, <b>2020</b>.