A Machine Learning Study of Polymer-Solvent Interactions

Ting-Li Liu; Lun-Yang Liu; Fang Ding; Yun-Qi Li

doi:10.1007/s10118-022-2716-2

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A Machine Learning Study of Polymer-Solvent Interactions

RESEARCH ARTICLE | Updated：2022-06-22

- A Machine Learning Study of Polymer-Solvent Interactions
- A Machine Learning Study of Polymer-Solvent Interactions
- Chinese Journal of Polymer Science 2022年40卷第7期页码：834-842
- Affiliations：
  
  a.State Key Laboratory of Polymer Physics and Chemistry, Changchun Institute of Applied Chemistry, Chinese Academy of Sciences, Changchun 130022, China
  b.School of Applied Chemistry and Engineering, University of Science and Technology of China, Hefei 230026, China
- Author bio：
  
  lyliu@ciac.ac.cn (L.Y.L.)
  yunqi@ciac.ac.cn (Y.Q.L.)
- Funds：
  
  The work was financially supported by the National Natural Science Foundation of China (Nos. 21774128, U1832177, 22173094, 51988102), CAS Key Research Program of Frontier Sciences (No. QYZDY-SSW-SLH027). We are grateful to Network and Computing Center, Changchun Institute of Applied Chemistry for essential support.
- DOI：10.1007/s10118-022-2716-2
  中图分类号：
- 收稿：2022-01-11，
  
  修回：2022-1-26，
  
  录用：2022-3-10，
  
  网络首发：2022-05-20，
  
  纸质出版：2022-07-01
- Accepted：
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Liu, T. L.; Liu, L. Y.; Ding, F.; Li, Y. Q. A machine learning study of polymer-solvent interactions. Chinese J. Polym. Sci. 2022, 40, 834–842

Ting-Li Liu, Lun-Yang Liu, Fang Ding, et al. A Machine Learning Study of Polymer-Solvent Interactions[J]. Chinese Journal of Polymer Science, 2022, 40(7): 834-842.
Liu, T. L.; Liu, L. Y.; Ding, F.; Li, Y. Q. A machine learning study of polymer-solvent interactions. Chinese J. Polym. Sci. 2022, 40, 834–842 DOI： 10.1007/s10118-022-2716-2.

Ting-Li Liu, Lun-Yang Liu, Fang Ding, et al. A Machine Learning Study of Polymer-Solvent Interactions[J]. Chinese Journal of Polymer Science, 2022, 40(7): 834-842. DOI： 10.1007/s10118-022-2716-2.

摘要

We constructed predictive models using machine learning for three polymer-solvent interaction parameters: Flory-Huggins interaction

Hildebrand solubility and the relative energy difference in Hansen solubility

and then compiled them into software (polySML-PSI). The impact of solvent properties on interaction and the applicability of commonly used empirical rules were evaluated further.

Abstract

Polymer-solvent interaction is a fundamentally important concept routinely described by the Flory-Huggins interaction (

)

Hildebrand solubility (Δ

) and the relative energy difference (RED) determined from Hansen solubility in experimental

theoretical and simulation studies. Here we performed a machine learning study based on a comprehensive and representative dataset covering the interaction pairs from 81 polymers and 1221 solvents. The regression models provide the coefficients of determination in the range of 0.86−0.94 and the classification models deliver the area under the receiver operating characteristic curve (AUCs) better than 0.93. These models were integrated into a newly developed software polySML-PSI. Important features including Log

molar volume and dipole are identified

and their non-linear

non-monotonic contributions to polymer-solvent interactions are presented. The widely known “like-dissolve-like” rule and two broadly used empirical equations to estimate

as a function of temperature or Hansen solubility are also evaluated

and the polymer-specified constants are presented. This study provides a quantitative reference and a tool to understand and utilize the concept of polymer-solvent interactions.

关键词

Keywords

references

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