Prediction of ionic liquid surface tension via a generalized interpretable Structure‐Surface Tension Relationship model

IF 3.5 3区工程技术 Q2 ENGINEERING, CHEMICAL

AIChE Journal Pub Date : 2024-08-07 DOI:10.1002/aic.18558

Wenguang Zhu, Runqi Zhang, Hai Liu, Leilei Xin, Jianhui Zhong, Hongru Zhang, Jianguang Qi, Yinglong Wang, Zhaoyou Zhu

{"title":"Prediction of ionic liquid surface tension via a generalized interpretable Structure‐Surface Tension Relationship model","authors":"Wenguang Zhu, Runqi Zhang, Hai Liu, Leilei Xin, Jianhui Zhong, Hongru Zhang, Jianguang Qi, Yinglong Wang, Zhaoyou Zhu","doi":"10.1002/aic.18558","DOIUrl":null,"url":null,"abstract":"Ionic liquids' (ILs) surface tension, vital in liquid interface research, faces challenges in measurement methods—time‐consuming and labor‐intensive. The Structure‐Surface Tension Relationship (SSTR) is crucial for understanding the surface tension laws of ionic liquids, helping to predict surface tension and design ionic liquids that meet target requirements. In this study, SMILES string and group contribution methods were used to generate descriptors, and the random forest and multi‐layer perceptron (MLP) models were cross combined with the two descriptor generation methods to establish the SSTR model, providing a comprehensive framework for predicting the surface tension of ionic liquids. String‐MLP excels with high accuracy (R2 = 0.995, RMSE = 0.686, AARD% = 0.71%) for diverse ILs' surface tension values. Meanwhile, the Shapley Additive exPlanning (SHAP) method was used to test the impact of different features on model prediction, increasing the transparency and interpretability of the model.","PeriodicalId":120,"journal":{"name":"AIChE Journal","volume":null,"pages":null},"PeriodicalIF":3.5000,"publicationDate":"2024-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"AIChE Journal","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1002/aic.18558","RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, CHEMICAL","Score":null,"Total":0}

引用次数: 0

Abstract

Ionic liquids' (ILs) surface tension, vital in liquid interface research, faces challenges in measurement methods—time‐consuming and labor‐intensive. The Structure‐Surface Tension Relationship (SSTR) is crucial for understanding the surface tension laws of ionic liquids, helping to predict surface tension and design ionic liquids that meet target requirements. In this study, SMILES string and group contribution methods were used to generate descriptors, and the random forest and multi‐layer perceptron (MLP) models were cross combined with the two descriptor generation methods to establish the SSTR model, providing a comprehensive framework for predicting the surface tension of ionic liquids. String‐MLP excels with high accuracy (R2 = 0.995, RMSE = 0.686, AARD% = 0.71%) for diverse ILs' surface tension values. Meanwhile, the Shapley Additive exPlanning (SHAP) method was used to test the impact of different features on model prediction, increasing the transparency and interpretability of the model.

查看原文本刊更多论文

通过广义可解释结构-表面张力关系模型预测离子液体表面张力

离子液体（ILs）的表面张力对液体界面研究至关重要，但在测量方法上却面临着费时费力的挑战。结构-表面张力关系（SSTR）对于理解离子液体的表面张力规律至关重要，有助于预测表面张力和设计符合目标要求的离子液体。本研究采用 SMILES 字符串法和组贡献法生成描述符，并将随机森林和多层感知器（MLP）模型与这两种描述符生成方法交叉结合，建立了 SSTR 模型，为预测离子液体的表面张力提供了一个全面的框架。String-MLP对不同离子液体的表面张力值具有很高的准确度（R2 = 0.995，RMSE = 0.686，AARD% = 0.71%）。同时，利用 Shapley Additive exPlanning（SHAP）方法测试了不同特征对模型预测的影响，增加了模型的透明度和可解释性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

AIChE Journal 工程技术-工程：化工

CiteScore

7.10

自引率

10.80%

发文量

411

审稿时长

3.6 months

期刊介绍： The AIChE Journal is the premier research monthly in chemical engineering and related fields. This peer-reviewed and broad-based journal reports on the most important and latest technological advances in core areas of chemical engineering as well as in other relevant engineering disciplines. To keep abreast with the progressive outlook of the profession, the Journal has been expanding the scope of its editorial contents to include such fast developing areas as biotechnology, electrochemical engineering, and environmental engineering. The AIChE Journal is indeed the global communications vehicle for the world-renowned researchers to exchange top-notch research findings with one another. Subscribing to the AIChE Journal is like having immediate access to nine topical journals in the field. Articles are categorized according to the following topical areas: Biomolecular Engineering, Bioengineering, Biochemicals, Biofuels, and Food Inorganic Materials: Synthesis and Processing Particle Technology and Fluidization Process Systems Engineering Reaction Engineering, Kinetics and Catalysis Separations: Materials, Devices and Processes Soft Materials: Synthesis, Processing and Products Thermodynamics and Molecular-Scale Phenomena Transport Phenomena and Fluid Mechanics.