首页|Prediction of Bond Dissociation Energy for Organic Molecules Based on a Machine-Learning Approach

Prediction of Bond Dissociation Energy for Organic Molecules Based on a Machine-Learning Approach

扫码查看
原文链接
  • 万方数据
  • 维普
Bond dissociation energy(BDE),which refers to the enthalpy change for the homolysis of a specific covalent bond,is one of the basic thermodynamic properties of molecules.It is very important for understanding chemical reactivities,chemical properties and chem-ical transformations.Here,a machine learning-based comprehensive BDE prediction model was established based on the iBonD ex-perimental BDE dataset and the calculated BDE dataset by St.John et al.Differential Structural and PhysicOChemical(D-SPOC)de-scriptors that reflected changes in molecules'structural and physicochemical features in the process of bond homolysis were de-signed as input features.The model trained with LightGBM algorithm gave a low mean absolute error(MAE)of 1.03 kcal/mol on the test set.The D-SPOC model could apply to accurate BDE prediction of phenol O—H bonds,uncommon N-SCF3 and O-SCF3 reagents,and β-C—H bonds in enamine intermediates.A fast online prediction platform was constructed based on the D-SPOC model,which could be found at http://isyn.luoszgroup.com/bde_prediction.

Bond dissociation energyMachine learningMolecular descriptorsPrediction

Yidi Liu、Yao Li、Qi Yang、Jin-Dong Yang、Long Zhang、Sanzhong Luo

展开 >

Center of Basic Molecular Science(CBMS),Department of Chemistry,Tsinghua University,Beijing 100084,China

Shanghai Institute of Organic Chemistry,University of Chinese Academy of Sciences,Chinese Academy of Sciences,Shanghai 200032,China

Haihe Laboratory of Sustainable Chemical Transformations,Tianjin 300192,China

2024

10.1002/cjoc.202400049

中国化学(英文版)
中国化学会 上海有机化学研究所

中国化学(英文版)

CSTPCD
影响因子：0.848
ISSN：1001-604X
年,卷(期)：2024.42(17)