Combining automated microfluidic experimentation with machine learning for efficient polymerization design
Combining automated microfluidic experimentation with machine learning for efficient polymerization design
复制标题
将自动微流控实验与机器学习相结合，实现高效的聚合设计
DOI：
10.1038/s42256-020-0166-5
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发表时间：
2020-04-01
影响因子：
23.8
通讯作者：
Hartman, Ryan L.
中科院分区：
文献类型：
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作者：
Rizkin, Benjamin A.;Shkolnik, Albert S.;Hartman, Ryan L.
了解聚合反应的挑战涉及系统的复杂性、与试剂相关的危险、操作的环境足迹以及反应空间的高度非线性拓扑结构。在这项工作中，我们旨在提供一种新的方法，使用机器学习辅助的自动化微化学反应来研究聚合反应。使用定制设计的快速原型微反应器，结合自动化和原位红外热像技术，进行高效、高速的实验，绘制茂锆聚合催化剂的反应空间图，并获得基本的动力学参数。化学废物减少了两个数量级，催化发现从几周减少到几个小时。贝叶斯正则化反向传播与动力学模型结合使用，以了解反应空间和所产生的技术经济拓扑。在这里，我们展示了高效的微流控技术与机器学习算法相结合，以获得关于复杂化学反应的高保真数据集。寻找聚合反应的最佳配料比例可能既耗时又浪费。一个集成了机器学习分析的自动化微反应器过程启动反应，测量产生的产率，并在没有人工干预的情况下进行自我清洁。它可以系统地测试试剂的浓度，以找到产量最高的组合，同时产生较少的废物。
Understanding polymerization reactions has challenges relating to the complexity of the systems, the hazards associated with the reagents, the environmental footprint of the operations and the highly nonlinear topologies of reaction spaces. In this work, we aim to present a new methodology for studying polymerization reactions using machine-learning-assisted automated microchemical reactors. A custom-designed rapidly prototyped microreactor is used in conjunction with automation and in situ infrared thermography for efficient, high-speed experimentation to map the reaction space of a zirconocene polymerization catalyst and obtain fundamental kinetic parameters. Chemical waste is decreased by two orders of magnitude and catalytic discovery is reduced from weeks to hours. Bayesian regularization backpropagation is used in conjunction with kinetic modelling to understand the reaction space and resultant technoeconomic topology. Here, we show that efficient microfluidic technology can be coupled with machine-learning algorithms to obtain high-fidelity datasets on a complex chemical reaction. Finding the best ratio of ingredients for polymerization reactions can be time consuming and wasteful. An automated microreactor process with integrated machine learning analysis initiates reactions, measures the resulting yield and cleans itself without human intervention. It can test concentrations of reagents systematically to find the combination with the highest production, while producing a low amount of waste.