2023-03-15 ノースカロライナ州立大学(NCState)
実証実験では、高品質の半導体ナノクリスタルを効率的に製造する方法を発見した。AlphaFlowは、AI技術の強化学習を採用して、化学実験を効率化し、数十のパラメータを扱う複雑な化学実験において、高度な機能性材料や分子を効率的に合成できる。AlphaFlowは、科学者の手作業に比べ、10倍以上高速で、化学物質の0.01%以下を使用して、同じ実験を実施できるという。
＜関連情報＞
- https://news.ncsu.edu/2023/03/alphaflow-speeds-chemical-discovery/
- https://www.nature.com/articles/s41467-023-37139-y
AlphaFlow：強化学習によって導かれた自己駆動型流体ラボを用いた、多段階化学の自律的な発見と最適化 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 & Milad Abolhasani
Nature Communications Published:14 March 2023
DOI:https://doi.org/10.1038/s41467-023-37139-y
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.
