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Estimation of the Flory-Huggins interaction parameter of polymer-solvent mixtures using machine learning

  • Computational Approaches for Materials Discovery and Development Research Letter
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Abstract

The Flory-Huggins interaction parameter \(\chi\) for polymer-solvent mixtures captures the nature of interactions and provides insights on solubility. \(\chi\) is usually estimated using experimental or (empirical) computational methods, which may be expensive, time-consuming or inaccurate. Here, we built a machine learning (ML) model to instantly predict temperature-dependent \(\chi\) for a given polymer-solvent pair. The ML model was trained using 1586 experimental polymer-solvent datapoints, and a hierarchical polymer and solvent fingerprinting scheme. Extensive testing has been performed to verify the accuracy and generality of this model. This work demonstrates an ML model that can progressively be improved as new data emerges.

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Data availability

The compiled \(\chi\) dataset has been made available in the Supplemental Information.

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Acknowledgments

The authors would like to thank ExxonMobil Research and Engineering for their support. We would also like to acknowledge Dr. Ronita Mathias from the Lively lab at Georgia Institute of Technology, for their valuable feedback. We also thank Joseph Kern and Chiho Kim from the Ramprasad Group at Georgia Institute of Technology, for their indispensable inputs.

Funding

This study was funded by ExxonMobil Research and Engineering.

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Authors and Affiliations

  1. School of Materials Science and Engineering, Georgia Institute of Technology, Atlanta, GA, 30332, USA

    Janhavi Nistane, Lihua Chen & Rampi Ramprasad

  2. School of Chemical & Biomolecular Engineering, Georgia Institute of Technology, Atlanta, GA, 30332, USA

    Youngjoo Lee & Ryan Lively

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  1. Janhavi Nistane
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  2. Lihua Chen
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Correspondence to Rampi Ramprasad.

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Nistane, J., Chen, L., Lee, Y. et al. Estimation of the Flory-Huggins interaction parameter of polymer-solvent mixtures using machine learning. MRS Communications 12, 1096–1102 (2022). https://doi.org/10.1557/s43579-022-00237-x

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