Prediction of Bond Dissociation Energy for Organic Molecules Based on a Machine-Learning Approach
Yidi Liu
Center of Basic Molecular Science (CBMS), Department of Chemistry, Tsinghua University, Beijing, 100084 China
These authors contributed equally to this work.
Search for more papers by this authorYao Li
Shanghai Institute of Organic Chemistry, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, 200032 China
These authors contributed equally to this work.
Search for more papers by this authorQi Yang
Center of Basic Molecular Science (CBMS), Department of Chemistry, Tsinghua University, Beijing, 100084 China
Search for more papers by this authorJin-Dong Yang
Center of Basic Molecular Science (CBMS), Department of Chemistry, Tsinghua University, Beijing, 100084 China
Haihe Laboratory of Sustainable Chemical Transformations, Tianjin, 300192 China
Search for more papers by this authorCorresponding Author
Long Zhang
Center of Basic Molecular Science (CBMS), Department of Chemistry, Tsinghua University, Beijing, 100084 China
Haihe Laboratory of Sustainable Chemical Transformations, Tianjin, 300192 China
E-mail: [email protected]; [email protected]Search for more papers by this authorCorresponding Author
Sanzhong Luo
Center of Basic Molecular Science (CBMS), Department of Chemistry, Tsinghua University, Beijing, 100084 China
Haihe Laboratory of Sustainable Chemical Transformations, Tianjin, 300192 China
E-mail: [email protected]; [email protected]Search for more papers by this authorYidi Liu
Center of Basic Molecular Science (CBMS), Department of Chemistry, Tsinghua University, Beijing, 100084 China
These authors contributed equally to this work.
Search for more papers by this authorYao Li
Shanghai Institute of Organic Chemistry, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, 200032 China
These authors contributed equally to this work.
Search for more papers by this authorQi Yang
Center of Basic Molecular Science (CBMS), Department of Chemistry, Tsinghua University, Beijing, 100084 China
Search for more papers by this authorJin-Dong Yang
Center of Basic Molecular Science (CBMS), Department of Chemistry, Tsinghua University, Beijing, 100084 China
Haihe Laboratory of Sustainable Chemical Transformations, Tianjin, 300192 China
Search for more papers by this authorCorresponding Author
Long Zhang
Center of Basic Molecular Science (CBMS), Department of Chemistry, Tsinghua University, Beijing, 100084 China
Haihe Laboratory of Sustainable Chemical Transformations, Tianjin, 300192 China
E-mail: [email protected]; [email protected]Search for more papers by this authorCorresponding Author
Sanzhong Luo
Center of Basic Molecular Science (CBMS), Department of Chemistry, Tsinghua University, Beijing, 100084 China
Haihe Laboratory of Sustainable Chemical Transformations, Tianjin, 300192 China
E-mail: [email protected]; [email protected]Search for more papers by this authorComprehensive Summary
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 chemical transformations. Here, a machine learning-based comprehensive BDE prediction model was established based on the iBonD experimental BDE dataset and the calculated BDE dataset by St. John et al. Differential Structural and PhysicOChemical (D-SPOC) descriptors that reflected changes in molecules’ structural and physicochemical features in the process of bond homolysis were designed 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.
Supporting Information
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Appendix S1: Supporting information |
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