Transfer Learning of Full Molecular Weight Distributions via High-Throughput Computer-Controlled Polymerization

Transfer Learning of Full Molecular Weight Distributions via High-Throughput Computer-Controlled Polymerization

Journal of Chemical Information and Modeling

10.1021/acs.jcim.3c00504

http://dx.doi.org/10.1021/acs.jcim.3c00504

Jin Da Tan, Balamurugan Ramalingam, Swee Liang Wong, Jayce Jian Wei Cheng, Yee-Fun Lim, Vijila Chellappan, Saif A. Khan, Jatin Kumar, Kedar Hippalgaonkar

General Chemistry, Computer Science Applications, General Chemical Engineering, Library and Information Sciences

11 July 2023

Tan, J. D., Ramalingam, B., Wong, S. L., Cheng, J. J. W., Lim, Y.-F., Chellappan, V., Khan, S. A., Kumar, J., & Hippalgaonkar, K. (2023). Transfer Learning of Full Molecular Weight Distributions via High-Throughput Computer-Controlled Polymerization. Journal of Chemical Information and Modeling, 63(15), 4560–4573. https://doi.org/10.1021/acs.jcim.3c00504

The skew and shape of the molecular weight distribution (MWD) of polymers have a significant impact on polymer physical properties. Standard summary metrics statistically derived from the MWD only provide an incomplete picture of the polymer MWD. Machine learning (ML) methods coupled with high-throughput experimentation (HTE) could potentially allow for the prediction of the entire polymer MWD without information loss. In our work, we demonstrate a computer-controlled HTE platform that is able to run up to 8 unique variable conditions in parallel for the free radical polymerization of styrene. The segmented-flow HTE system was equipped with an inline Raman spectrometer and offline size exclusion chromatography (SEC) to obtain time-dependent conversion and MWD, respectively. Using ML forward models, we first predict monomer conversion, intrinsically learning varying polymerization kinetics that change for each experimental condition. In addition, we predict entire MWDs including the skew and shape as well as SHAP analysis to interpret the dependence on reagent concentrations and reaction time. We then used a transfer learning approach to use the data from our high-throughput flow reactor to predict batch polymerization MWDs with only three additional data points. Overall, we demonstrate that the combination of HTE and ML provides a high level of predictive accuracy in determining polymerization outcomes. Transfer learning can allow exploration outside existing parameter spaces efficiently, providing polymer chemists with the ability to target the synthesis of polymers with desired properties.

Publisher Copyright

This research / project is supported by the A*STAR - AME Programmatic Fund, Accelerated Materials Development for Manufacturing Program
Grant Reference no. : A1898b0043

This document is the Accepted Manuscript version of a Published Work that appeared in final form in Journal of Chemical Information and Modeling, copyright © American Chemical Society after peer review and technical editing by the publisher. To access the final edited and published work see doi.org/10.1021/acs.jcim.3c00504

https://oar.a-star.edu.sg/communities-collections/articles/19292

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Institute of Materials Research and Engineering