Neural Network Model for Predicting the Atomization Energy of Multi-Atomic Molecules Based on Sorted Coulomb Matrices

This paper develops a method for predicting the atomization energy Eat of multi-atomic compounds by representing the molecular configuration as a sorted Coulomb matrix M(Ri, Zi). A dataset of 7165 molecules with H, C, N, O, S content is analyzed and the input data for training is generated. The optimal layer configuration and hyperparameters values were found for the fully-connected neural network model. The minimum mean absolute error MAE ≈ 7.66 kcal/mol was achieved in predicting the atomization energy Eat.