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

作　　者：Chaoqin Chu Qinkun Xiao Chaozheng He Chen Chen Lu Li Junyan Zhao Jinzhou Zheng Yinhuan Zhang 

机构地区：[1]School of Mechanical and Electrical Engineering,Xi’an Technological University,Xi’an 710021,China [2]School of Electrical and Information Engineering,Xi’an Technological University,Xi’an 710021,China [3]School of Materials Science and Chemical Engineering,Xi’an Technological University,Xi’an 710021,China

出　　处：《Chinese Chemical Letters》2024年第1期505-509,共5页中国化学快报（英文版）

基　　金：the Nature Science Foundation of China(Nos.61671362 and 62071366).

摘　　要：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.

关 键 词：Structure prediction Atomization energy Deep learning Coulomb matrix NPN END-TO-END 

分 类 号：TB30[一般工业技术—材料科学与工程] TP18[自动化与计算机技术—控制理论与控制工程]