A novel method for atomization energy prediction based on natural-parameter network

Abstract

Atomization energy (AE) is an important indicator for measuring material stability and reactivity, which refers to the energy change when a polyatomic molecule decomposes into its constituent atoms. Predicting AE based on the structural information of molecules has been a focus of researchers, but existing methods have limitations such as being time-consuming or requiring complex preprocessing and large amounts of training data. Deep learning (DL), a new branch of machine learning (ML), has shown promise in learning internal rules and hierarchical representations of sample data, making it a potential solution for AE prediction. To address this problem, we propose a natural-parameter network (NPN) approach for AE prediction. This method establishes a clearer statistical interpretation of the relationship between the network's output and the given data. We use the Coulomb matrix (CM) method to represent each compound as a structural information matrix. Furthermore, we also designed an end-to-end predictive model. Experimental results demonstrate that our method achieves excellent performance on the QM7 and BC2P datasets, and the mean absolute error (MAE) obtained on the QM7 test set ranges from 0.2 kcal/mol to 3 kcal/mol. The optimal result of our method is approximately an order of magnitude higher than the accuracy of 3 kcal/mol in published works. Additionally, our approach significantly accelerates the prediction time. Overall, this study presents a promising approach to accelerate the process of predicting structures using DL, and provides a valuable contribution to the field of chemical energy prediction.