4.2 Review

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

Journal

MRS COMMUNICATIONS
Volume 12, Issue 6, Pages 1096-1102

Publisher

SPRINGER HEIDELBERG
DOI: 10.1557/s43579-022-00237-x

Keywords

-

Funding

  1. ExxonMobil Research and Engineering

Ask authors/readers for more resources

This study presents a machine learning model that can instantly predict the temperature-dependent Flory-Huggins interaction parameter for polymer-solvent mixtures. The model has been trained using a large dataset of experimental data and demonstrates high accuracy and generality.
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.

Authors

I am an author on this paper
Click your name to claim this paper and add it to your profile.

Reviews

Primary Rating

4.2
Not enough ratings

Secondary Ratings

Novelty
-
Significance
-
Scientific rigor
-
Rate this paper

Recommended

No Data Available
No Data Available