Predicting the Self-Diffusion Coefficient of Liquids Based on Backpropagation Artificial Neural Network: A Quantitative Structure–Property Relationship Study-Reference-Cited by-同舟云学术

Predicting the Self-Diffusion Coefficient of Liquids Based on Backpropagation Artificial Neural Network: A Quantitative Structure–Property Relationship Study

Published:2022-11-29 Issue:48 Volume:61 Page:17697-17706
ISSN:0888-5885
Container-title:Industrial & Engineering Chemistry Research
language:en
Short-container-title:Ind. Eng. Chem. Res.

Author:

Zeng Fazhan¹,Wan Ren¹,Xiao Yongjun¹,Song Fan¹,Peng Changjun¹^ORCID,Liu Honglai¹^ORCID

Affiliation:

1. School of Chemistry & Molecular Engineering, East China University of Science and Technology, Shanghai200237, China

Funder

National Natural Science Foundation of China

Publisher

American Chemical Society (ACS)

Subject

Industrial and Manufacturing Engineering,General Chemical Engineering,General Chemistry

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.iecr.2c03342

Reference64 articles.

1. PFG-NMR and MD Simulation Study of Self-Diffusion Coefficients of Binary and Ternary Mixtures Containing Cyclohexane, Ethanol, Acetone, and Toluene

2. Obtaining transport diffusion coefficients from self-diffusion coefficients in nanoporous adsorption systems

3. Prediction of Composition-Dependent Self-Diffusion Coefficients in Binary Liquid Mixtures: The Missing Link for Darken-Based Models

4. The fractional Stokes–Einstein equation: Application to Lennard-Jones, molecular, and ionic liquids

5. Revisiting the Stokes-Einstein relation without a hydrodynamic diameter

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