首页|Hybrid data-driven and physics-based modeling for viscosity prediction of ionic liquids

Hybrid data-driven and physics-based modeling for viscosity prediction of ionic liquids

扫码查看
原文链接
  • NETL
  • NSTL
  • 万方数据
Viscosity is one of the most important fundamental properties of fluids.However,accurate acquisition of viscosity for ionic liquids(ILs)remains a critical challenge.In this study,an approach integrating prior physical knowledge into the machine learning(ML)model was proposed to predict the viscosity reliably.The method was based on 16 quantum chemical descriptors determined from the first principles calculations and used as the input of the ML models to represent the size,structure,and interactions of the ILs.Three strategies based on the residuals of the COSMO-RS model were created as the output of ML,where the strategy directly using experimental data was also studied for comparison.The performance of six ML algorithms was compared in all strategies,and the CatBoost model was identified as the optimal one.The strategies employing the relative deviations were superior to that using the absolute deviation,and the relative ratio revealed the systematic prediction error of the COSMO-RS model.The CatBoost model based on the relative ratio achieved the highest prediction accuracy on the test set(R2=0.9999,MAE=0.0325),reducing the average absolute relative deviation(AARD)in modeling from 52.45％to 1.54％.Features importance analysis indicated the average energy correction,solvation-free energy,and polarity moment were the key influencing the systematic deviation.© 2024 Institute of Process Engineering,Chinese Academy of Sciences.Publishing services by Elsevier B.V.on behalf of KeAi Communi-cations Co.,Ltd.This is an open access article under the CC BY-NC-ND license(http://creativecommons.org/licenses/by-nc-nd/4.0/).

ViscosityFundamental propertyIonic liquidsCOSMO-RSMachine learning

Jing Fan、Zhengxing Dai、Jian Cao、Liwen Mu、Xiaoyan Ji、Xiaohua Lu

展开 >

State Key Laboratory of Materials-Oriented and Chemical Engineering,Nanjing Tech University,Nanjing,211816,China

Energy Engineering,Division of Energy Science,Luleå University of Technology,Luleå,97187,Sweden

2024

10.1016/j.gee.2024.01.007

绿色能源与环境(英文)

绿色能源与环境(英文)

CSTPCD
ISSN：
年,卷(期)：2024.9(12)