Insight to the prediction of CO2 solubility in ionic liquids based on the interpretable machine learning model-Reference-Cited by-同舟云学术

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Insight to the prediction of CO2 solubility in ionic liquids based on the interpretable machine learning model

Published:2024-09 Issue: Volume:297 Page:120266
ISSN:0009-2509
Container-title:Chemical Engineering Science
language:en
Short-container-title:Chemical Engineering Science

Author:

Yang Ao^ORCID,Sun Shirui,Su Yang^ORCID,Kong Zong Yang,Ren Jingzheng,Shen Weifeng^ORCID

Publisher

Elsevier BV

Reference37 articles.

1. Computer-aided molecular design: An introduction and review of tools, applications, and solution techniques;Austin;Chem. Eng. Res. Des.,2016

2. Graph Neural Networks and Structural Information on Ionic Liquids: A Cheminformatics Study on Molecular Physicochemical Property Prediction;Baran;J. Phys. Chem. B,2023

3. Optimal Molecular Design of Ionic Liquids for High-Purity Bioethanol Production;Chávez-Islas;Ind. Eng. Chem. Res.,2011

4. Integrated ionic liquid and process design involving azeotropic separation processes;Chen;Chem. Eng. Sci.,2019

5. Computer-aided design of ionic liquids for hybrid process schemes;Chen;Comput. Chem. Eng.,2019

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Development of an interpretable QSPR model to predict the octanol-water partition coefficient based on three artificial intelligence algorithms;Green Chemical Engineering;2024-07

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