C–H bond dissociation enthalpy prediction with machine learning reinforced semi-empirical quantum mechanical calculations

Author:

Kaneko Miki1,Takano Yu2ORCID,Saito Toru2

Affiliation:

1. Department of Bioinformation Sciences, Faculty of Information Sciences, Hiroshima City University , 3-4-1 Ozuka-Higashi, Asa-Minami-Ku , Hiroshima, 731-3194, Japan

2. Department of Bioinformation Sciences, Graduate School of Information Sciences, Hiroshima City University , 3-4-1 Ozuka-Higashi, Asa-Minami-Ku , Hiroshima, 731-3194, Japan

Abstract

Abstract We introduce a combined fast semi-empirical quantum mechanical and machine learning (SQM/ML) approach capable of matching the C–H bond dissociation enthalpies (BDEs) computed with the highly accurate (RO)CBS-QB3 method. The usefulness of our proposed SQM/ML model is corroborated by the fact that a single C–H BDE of a molecule is calculated in seconds and the mean absolute error amounts to only 1 to 2 kcal/mol.

Funder

Grant-in-Aid for Scientific Research

Japan Society for the Promotion of Science

Grant-in-Aid for Transformative Research Areas

Ministry of Education, Culture, Sports, Science and Technology

Publisher

Oxford University Press (OUP)

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