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Combining automated microfluidic experimentation with machine learning for efficient polymerization design

Abstract

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.

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Fig. 1: Overview of the chemistry.
Fig. 2: Reactor schematic and computational and experimental verification of performance.
Fig. 3: Process flow diagram for an automated thermographic microreactor system used in the understanding of metallocene catalysts.
Fig. 4: Flowcharts for experimental control and data handling.
Fig. 5: Experimental results for catalytic productivity and kinetic rate constants.
Fig. 6: ANN results for catalytic productivity over various concentrations and temperatures.

Data availability

The datasets generated and/or analysed during the current study are available in the Zenodo repository, http://doi.org/110.5281/zenodo.3706730[50].

Code availability

The code used for data analysis in the current study is publicly available in the Zenodo repository, http://doi.org/110.5281/zenodo.3706730[50]. The code used for experimental control is available in the Zenodo repository, http://doi.org/zenodo.3706734[51]. This code is not publicly available due to inclusion of third party code but is available upon reasonable request from the corresponding author.

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Acknowledgements

This material is based on work supported by the National Science Foundation under grant no. CBET-1701393. Any opinions, findings and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation.

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Conceptualization was provided by R.L.H., data curation by B.A.R., formal analysis by B.A.R. and A.S.S., funding acquisition by R.L.H., investigation by B.A.R. and A.S.S., methodology by B.A.R. and N.J.F., project administration by B.A.R. and R.L.H., resources by R.L.H., software by B.A.R., A.S.S. and N.J.F., supervision by R.L.H., validation by B.A.R. and A.S.S., visualization by B.A.R., A.S.S. and R.L.H., writing of the original draft by B.A.R. and A.S.S. and review and editing by R.L.H.

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Correspondence to Ryan L. Hartman.

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Supplementary methods, Figs. 1–5, Tables 1 and 2, discussion and analysis.

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Rizkin, B.A., Shkolnik, A.S., Ferraro, N.J. et al. Combining automated microfluidic experimentation with machine learning for efficient polymerization design. Nat Mach Intell 2, 200–209 (2020). https://doi.org/10.1038/s42256-020-0166-5

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