Journal
MRS COMMUNICATIONS
Volume 12, Issue 6, Pages 1096-1102Publisher
SPRINGER HEIDELBERG
DOI: 10.1557/s43579-022-00237-x
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Funding
- ExxonMobil Research and Engineering
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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.
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