AlphaFlow: autonomous discovery and optimization of multi-step chemistry using a self-driven fluidic lab guided by reinforcement learning

Amanda A. Volk, Robert W. Epps, Daniel T. Yonemoto, Benjamin S. Masters, Felix N. Castellano, Kristofer G. Reyes and Milad Abolhasani ()
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Amanda A. Volk: North Carolina State University
Robert W. Epps: North Carolina State University
Daniel T. Yonemoto: North Carolina State University
Benjamin S. Masters: North Carolina State University
Felix N. Castellano: North Carolina State University
Kristofer G. Reyes: University at Buffalo
Milad Abolhasani: North Carolina State University

Nature Communications, 2023, vol. 14, issue 1, 1-16

Abstract: Abstract Closed-loop, autonomous experimentation enables accelerated and material-efficient exploration of large reaction spaces without the need for user intervention. However, autonomous exploration of advanced materials with complex, multi-step processes and data sparse environments remains a challenge. In this work, we present AlphaFlow, a self-driven fluidic lab capable of autonomous discovery of complex multi-step chemistries. AlphaFlow uses reinforcement learning integrated with a modular microdroplet reactor capable of performing reaction steps with variable sequence, phase separation, washing, and continuous in-situ spectral monitoring. To demonstrate the power of reinforcement learning toward high dimensionality multi-step chemistries, we use AlphaFlow to discover and optimize synthetic routes for shell-growth of core-shell semiconductor nanoparticles, inspired by colloidal atomic layer deposition (cALD). Without prior knowledge of conventional cALD parameters, AlphaFlow successfully identified and optimized a novel multi-step reaction route, with up to 40 parameters, that outperformed conventional sequences. Through this work, we demonstrate the capabilities of closed-loop, reinforcement learning-guided systems in exploring and solving challenges in multi-step nanoparticle syntheses, while relying solely on in-house generated data from a miniaturized microfluidic platform. Further application of AlphaFlow in multi-step chemistries beyond cALD can lead to accelerated fundamental knowledge generation as well as synthetic route discoveries and optimization.

Date: 2023
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DOI: 10.1038/s41467-023-37139-y

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