热扩散率
稀释
支持向量机
人工智能
水溶液
生物系统
扩散
分子
高斯分布
计算机科学
机器学习
材料科学
热力学
化学
计算化学
物理化学
有机化学
物理
生物
作者
Beyene Hagos Aregawi,Diana Tazeddinova,Chia‐Hung Su,A.S. El-Shafay,May Alashwal,Bassem F. Felemban,Mohammed Zwawi,Mohammed Algarni,Fu‐Ming Wang
标识
DOI:10.1016/j.molliq.2022.118763
摘要
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.
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