Abstract:
Accelerating the discovery of new advanced functional materials, as well as green and sustainable ways to synthesize and manufacture them, will have a profound impact on the worldwide challenges in renewable 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. The time-, resource-, and labor-intensive nature of current experimental sciences necessitates the development and implementation of new strategies to accelerate the pace of discovery. Recent advances in reaction miniaturization, lab automation, and data science provide an exciting opportunity to reshape the discovery and manufacturing of new materials related to energy transition and sustainability. In this talk, I will present an overview of our recent efforts to establish a 'self-driving fluidic lab (SDFL)' through integration of flow chemistry, robotics, online characterization, and machine learning (ML) for autonomous discovery and manufacturing of emerging advanced functional materials with multi-step chemistries.1-5 I will discuss how modularization of different material 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 colloidal quantum dots 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 materials research through facile switching from the reaction exploration/exploitation to smart manufacturing mode.
References.
[1] Abolhasani, M.; Kumacheva, E. The rise of self-driving labs in chemical and materials sciences. Nature Synthesis, 2, 483–492, 2023.
[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. Nature Communications, 14 (1), 1403, 2023.
[3] Volk, A. A.; Abolhasani, M. Autonomous flow reactors for discovery and invention. Trends in Chemistry, 3 (7), 519-522, 2021.
[4] Delgado-Licona, F.; Abolhasani, M. Research Acceleration in Self-Driving Labs: Technological Roadmap toward Accelerated Materials and Molecular Discovery. Advanced Intelligent Systems, 5, 2200331, 2023.
[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. Advanced Materials, 32 (30), 2001626, 2020.
Biosketch: 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.
In-Person Location: Building 440, A105/A106
Virtual Link: https://argonne.zoomgov.com/j/1613912289