Accelerated Materials Discovery, Optimization and Manufacturing with Autonomous Robotic Experimentation in Flow

Accelerating materials discovery as well as green and sustainable ways to manufacture them will have a profound impact on the worldwide challenges in energy and sustainability. The current human-dependent paradigm of experimental research in chemical and materials sciences fails to identify material solutions for worldwide challenges in a short timeframe. This limitation necessitates the development and implementation of new strategies to accelerate the pace of materials discovery. Recent advances in reaction miniaturization, automated experimentation, and artificial intelligence (AI) provide an exciting opportunity to reshape the discovery, development, and manufacturing of new advanced functional materials related to energy transition and sustainability. In this talk, I will present an<i> Autonomous Fluidic Lab</i> for accelerated discovery, optimization, and manufacturing of emerging advanced functional materials with multi-step chemistries, through the integration of flow chemistry, online characterization, and AI.^1-5 I will discuss how modularization of different synthesis and processing stages in tandem with constantly evolving AI-assisted modeling and decision-making under uncertainty can enable resource-efficient navigation through high-dimensional experimental design spaces. Example applications of the Autonomous Fluidic Lab for the fast-tracked synthesis of clean energy nanomaterials will be presented to illustrate the potential of autonomous labs in reducing materials discovery timeframe from &gt;10 years to a few months (or even weeks).<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>, 5(4), 2200331, <b>2023</b>.<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>.