A neural network protocol for predicting molecular bond energy

• 来源期刊: 中国科学:化学（英文版）
• 年，卷（期）: 2019,（12）
• 页码范围: 1698-1703
• 页数: 6页
• 语种: 英文

摘要

Molecular bond energy is a key parameter for analyzing the properties of chemical activity,stability and flexibility.Calculating bond energy is a challenge due to the cost of first-principles simulations and unsatisfactory prediction using empirical formula.Here we show that a neural network (NN) machine-learning method can achieve quick prediction of bond energies of organic molecules.Using atomic species and charge information as descriptors,we trained a NN protocol and applied it to predict the bond energy in a certain chemical bond that agreed with density functional theory calculations.This protocol also provided a way to evaluate the effects of different methods of atomic charge analysis on NN training.Trained to accurately estimate bond energies,this NN protocol provides a cost-effective tool for optimizing chemical reactions,accelerating molecular design,and other important applications.