چکیده مقاله
This study integrates computational chemistry and machine learning to investigate the relationship between the chemical functional groups and the heat capacity values of amine compounds The heat capacity of amine compounds was estimated using a hybrid method that includes a simple group contribution GC method implemented in a radial basis function neural network RBF NN Genetic function approximation GFA as a proper computational method was used for selection the most important functional groups and linear model developing The nonlinear relation between the selected functional groups and the heat capacity values of amine compounds was determined by RBF NN The validation of GC models illustrated that the squared correlation coefficient R2 between predicted and experimental values were 0 929 and 0 954 for GFA and RBF NN, respectively The obtained results in this article suggest that by using machine learning approach, it is possible to obtain a good estimation of the liquid heat capacity values of amine compounds
کلیدواژهها
نویسندگان
Aboozar Khajeh
Department of Chemical Engineering, Birjand University of Technology, Birjand, Iran
شیوه ارجاع
Khajeh, Aboozar,1404,Application of machine learning approach for prediction the heat capacity of amine,The Second National Conference on Data Science in Engineering Applications,Tabriz
ارائهشده در
مجموعه مقالات دومین کنفرانس ملی علم داده در کاربردهای مهندسی17 مهر 1404 · تبریز