{"title":"Explainable Prediction of Hydrophilic/Hydrophobic Property of Polymer Brush Surfaces by Chemical Modeling and Machine Learning","authors":"Shiwei Su, Tsukuru Masuda* and Madoka Takai*, ","doi":"10.1021/acs.jpcb.3c08422","DOIUrl":null,"url":null,"abstract":"<p >Polymer informatics has attracted increasing attention as a specialized branch of material informatics. Hydrophilicity/hydrophobicity is one of the most important properties of interfaces involved in antifouling, self-cleaning, antifogging, oil/water separation, protein adsorption, and bioseparation. Establishing a quantitative structure–property relationship for the hydrophilicity/hydrophobicity of polymeric interfaces could significantly benefit from machine learning modeling. In this study, we aimed to construct machine learning models that could predict the static water contact angle (CA) as an indicator of hydrophilicity/hydrophobicity based on a data set of polymer brushes. The features of the polymer brush surfaces were numerically described using their grafted structures (thickness) and molecular descriptors derived from their chemical structures. We achieved accurate prediction and understanding of important parameters by employing appropriate molecular descriptors considering the Pearson correlation and machine learning models trained with nested cross-validation. The model interpretation by Shapley additive extension analysis indicated that the amount of partial polar/nonpolar structure in the molecule as well as the averaged hydrophobicity represented by MolLogP plays an important role in determining the CA. Moreover, the model can predict the CAs of polymer brushes composed of chemical structures that are not present in existing databases. The CA values of the hypothetical polymer brushes are predicted.</p>","PeriodicalId":60,"journal":{"name":"The Journal of Physical Chemistry B","volume":null,"pages":null},"PeriodicalIF":2.8000,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"The Journal of Physical Chemistry B","FirstCategoryId":"1","ListUrlMain":"https://pubs.acs.org/doi/10.1021/acs.jpcb.3c08422","RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"CHEMISTRY, PHYSICAL","Score":null,"Total":0}
引用次数: 0
Abstract
Polymer informatics has attracted increasing attention as a specialized branch of material informatics. Hydrophilicity/hydrophobicity is one of the most important properties of interfaces involved in antifouling, self-cleaning, antifogging, oil/water separation, protein adsorption, and bioseparation. Establishing a quantitative structure–property relationship for the hydrophilicity/hydrophobicity of polymeric interfaces could significantly benefit from machine learning modeling. In this study, we aimed to construct machine learning models that could predict the static water contact angle (CA) as an indicator of hydrophilicity/hydrophobicity based on a data set of polymer brushes. The features of the polymer brush surfaces were numerically described using their grafted structures (thickness) and molecular descriptors derived from their chemical structures. We achieved accurate prediction and understanding of important parameters by employing appropriate molecular descriptors considering the Pearson correlation and machine learning models trained with nested cross-validation. The model interpretation by Shapley additive extension analysis indicated that the amount of partial polar/nonpolar structure in the molecule as well as the averaged hydrophobicity represented by MolLogP plays an important role in determining the CA. Moreover, the model can predict the CAs of polymer brushes composed of chemical structures that are not present in existing databases. The CA values of the hypothetical polymer brushes are predicted.
聚合物信息学作为材料信息学的一个专业分支,已引起越来越多的关注。亲水性/疏水性是涉及防污、自洁、防雾、油/水分离、蛋白质吸附和生物分离的界面的最重要特性之一。为聚合物界面的亲水性/疏水性建立定量的结构-性能关系,将极大地受益于机器学习建模。在本研究中,我们旨在根据聚合物刷数据集构建机器学习模型,以预测静态水接触角(CA),作为亲水性/疏水性的指标。聚合物刷表面的特征是利用其接枝结构(厚度)和从其化学结构中提取的分子描述符进行数值描述的。通过使用适当的分子描述符(考虑到皮尔逊相关性)和经过嵌套交叉验证训练的机器学习模型,我们实现了对重要参数的准确预测和理解。通过 Shapley 加性延伸分析对模型的解释表明,分子中部分极性/非极性结构的数量以及 MolLogP 所代表的平均疏水性在决定 CA 方面起着重要作用。此外,该模型还能预测由现有数据库中不存在的化学结构组成的聚合物刷的 CA 值。该模型预测了假定聚合物刷的 CA 值。
期刊介绍:
An essential criterion for acceptance of research articles in the journal is that they provide new physical insight. Please refer to the New Physical Insights virtual issue on what constitutes new physical insight. Manuscripts that are essentially reporting data or applications of data are, in general, not suitable for publication in JPC B.