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Reliable and robust f(T,P,I)-QSPR models for ionic liquids enabled by balancing data distribution and LOIO-CV
  • +3
  • Xiao Liu,
  • Mengxian Yu,
  • Qingzhu Jia,
  • Fangyou Yan,
  • Yin-Ning Zhou,
  • Qiang Wang
Xiao Liu
Tianjin University of Science and Technology

Corresponding Author:[email protected]

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Mengxian Yu
Tianjin University of Science and Technology
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Qingzhu Jia
Tianjin University of Science and Technology
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Fangyou Yan
Tianjin University of Science and Technology
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Yin-Ning Zhou
Shanghai Jiao Tong University
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Qiang Wang
Tianjin University of Science and Technology
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Abstract

The thermodynamic properties at variable temperature and pressure, such as density (ρ) and viscosity (η) are necessary in chemical process design. The quantitative structure-property relationship (QSPR) is a quick and accurate method to obtain the properties from a large number of potential ionic liquids (ILs). The QSPR models for ρ and η may have “pseudo-high” robustness validated by leave-one-out cross-validation (LOO-CV) and weakened stability with the unbalanced data point distribution. A rigorous model evaluation method named the leave-one-ion-out cross-validation (LOIO-CV) was proposed to evaluate robustness of ILs QSPR models. Balancing the distribution of data points in ILs, two f(T,P,I)-QSPR models were developed with norm index (I) to predict ρ and η of ILs at variable temperature and pressure. LOIO-CV method can enhance the stability QSPR model in predicting the properties of ILs with new cations and anions, which is essential for data driven design of ILs.