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Machine Learning to Predict Homolytic Dissociation Energies of C−H Bonds
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Resumo(s)
Random Forest (RF) QSPR models were developed with a data set of homolytic bond dissociation energies (BDE) previously calculated by B3LYP/6-311++G(d,p)//DFTB for 2263 sp3C−H covalent bonds. The best set of attributes consisted in 114 descriptors of the carbon atom (counts of atom types in 5 spheres around the kernel atom and ring descriptors). The optimized model predicted the DFT-calculated BDE of an independent test set of 224 bonds with MAE=2.86 kcal/mol. A new data set of 409 bonds from the iBonD database (http://ibond.nankai.edu.cn) was predicted by the RF with a modest MAE (5.36 kcal/mol) but a relatively high R2 (0.75) against experimental energies. A prediction scheme was explored that corrects the RF prediction with the average deviation observed for the k nearest neighbours (KNN) in an additional memory of experimental data. The corrected predictions achieved MAE=2.22 kcal/mol for an independent test set of 145 bonds and the corresponding experimental bond energies.
Descrição
JAS thanks David Ponting and co‐workers at Lhasa Limited for useful suggestions and discussions. This work was also supported by the National Natural Science Foundation of China [Grant number 21875061, 21975066] and the program for Science & Technology Innovation Team in Universities of Henan Province [Grant number 19IRTSTHN029].
Publisher Copyright:
© 2022 The Authors. Molecular Informatics published by Wiley-VCH GmbH.
Palavras-chave
bond energy density functional calculations learning transfer machine learning quantitative structure-property relationship Structural Biology Molecular Medicine Drug Discovery Computer Science Applications Organic Chemistry