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作 者:Ting-Li Liu Lun-Yang Liu Fang Ding Yun-Qi Li
机构地区:[1]State Key Laboratory of Polymer Physics and Chemistry,Changchun Institute of Applied Chemistry,Chinese Academy of Sciences,Changchun,130022,China [2]School of Applied Chemistry and Engineering,University of Science and Technology of China,Hefei,230026,China
出 处:《Chinese Journal of Polymer Science》2022年第7期834-842,共9页高分子科学(英文版)
基 金: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);Network and Computing Center,Changchun Institute of Applied Chemistry for essential support。
摘 要:Polymer-solvent interaction is a fundamentally important concept routinely described by the Flory-Huggins interaction(χ),Hildebrand solubility(Δδ)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 81polymers 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 P,molar volume and dipole are identified,and their non-linear,nonmonotonic contributions to polymer-solvent interactions are presented.The widely known“like-dissolve-like”rule and two broadly used empirical equations to estimateχas 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:Flory-Huggins interaction Hildebrand solubility Hansen solubility Machine learning Prediction
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