Development of a machine learning computational technique for estimation of molecular diffusivity of nonelectrolyte organic molecules in aqueous media
Hagos Aregawi B. Diana T. Su C.-H. El-Shafay A.S. Alashwal M. Felemban B.F. Zwawi M. Algarni M. Wang F.-M.
1 May 2022Elsevier B.V.
Journal of Molecular Liquids
2022#353
A model was developed based on machine learning technique to predict the molecular diffusivity of organic compounds in water at infinite dilution. The considered organic compounds are nonelectrolyte and diverse to provide a comprehensive method for prediction of diffusivity at infinite dilution and temperature of 25 °C. Two different machine learning techniques including Tree fine and Fine Gaussian SVM are utilized in this work for estimation of molecular diffusivity of organic molecules into aqueous media. The inputs parameters were taken as the functional groups of the molecule which is equal to 148 groups. To train the employed machine learning algorithms, 3000 datasets are randomly chosen and then randomized again using the algorithms. The results of simulations indicated that the Fine Tree model outperformed the SVM method with great accuracy and high R coefficients in estimation of diffusion coefficients.
Artificial intelligence , Computational modeling , Molecular diffusivity , SVM
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Graduate Institute of Applied Science and Technology, National Taiwan University of Science and Technology, Taipei, Taiwan
Department of Chemical Engineering, Ming Chi University of Technology, New Taipei City, Taiwan
Department of Technology and Catering Organization, South Ural State University, Chelyabinsk, Russian Federation
Zhangir Khan Agrarian Technical University, Uralsk, Kazakhstan
Department of Mechanical Engineering, College of Engineering, Prince Sattam bin Abdulaziz University, Alkharj, 16273, Saudi Arabia
Department of Computer Science, Jeddah International College, Jeddah, Saudi Arabia
Department of Mechanical Engineering, College of Engineering, Taif University, P.O. Box 11099, Taif, 21955, Saudi Arabia
Mechanical Engineering Department, Faculty of Engineering, King Abdulaziz University, P.O. Box 344, Rabigh, 21911, Saudi Arabia
Graduate Institute of Applied Science and Technology
Department of Chemical Engineering
Department of Technology and Catering Organization
Zhangir Khan Agrarian Technical University
Department of Mechanical Engineering
Department of Computer Science
Department of Mechanical Engineering
Mechanical Engineering Department
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