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.Predict-ing 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 com-pound 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 0.2kcal/mol to 3kcal/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 pre-diction 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.
NETL
NSTL
维普
万方数据