Prediction of enthalpy of fusion of pure compounds using an Artificial Neural Network-Group Contribution method

In this work, the Artificial Neural Network-Group Contribution (ANN-GC) method is applied to estimate the enthalpy of fusion of pure chemical compounds at their normal melting point. 4157 pure compounds from various chemical families are investigated to propose a comprehensive and predictive model. The obtained results show the Squared Correlation Coefficient (R2) of 0.999, Root Mean Square Error of 0.82 kJ/mol, and average absolute deviation lower than 2.65% for the estimated properties from existing experimental values.

► An Artificial Neural Network-Group Contribution method is presented for prediction of enthalpy of fusion of pure compounds at their normal melting point. ► Validity of the model is confirmed using a large evaluated data set containing 4157 pure compounds. ► The average percent error of the model is equal to 2.65% in comparison with the experimental data.