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Alfabet_MS_20191106.pdf (1.48 MB)

Prediction of Homolytic Bond Dissociation Enthalpies for Organic Molecules at near Chemical Accuracy with Sub-Second Computational Cost

preprint
revised on 06.11.2019 and posted on 15.11.2019 by Peter St. John, Yanfei Guan, Yeonjoon Kim, Seonah Kim, Robert Paton
Bond dissociation enthalpies (BDEs) of organic molecules play a fundamental role in determining chemical reactivity. However, BDE computations at sufficiently high levels of quantum mechanical (QM) theory require substantial computing resources. We have therefore developed A machine-Learning derived, Fast, Accurate Bond dissociation Enthalpy Tool (ALFABET), capable of accurately predicting BDEs for organic molecules in a fraction of a second. Automated density functional theory (DFT) calculations at the M06-2X/def2-TZVP level of theory were performed for 42,577 small organic molecules, resulting in a dataset of 290,664 BDEs. A graph neural network was trained on a subset of these results, achieving a mean absolute error of 0.58 kcal/mol for the BDE values of unseen molecules. An interface for the developed prediction tool is available online at https://ml.nrel.gov/bde. The model rapidly and accurately predicts major sites of hydrogen abstraction in metabolism of drug-like molecules and determines the dominant molecular fragmentation pathways during soot formation.

Funding

DE-AC36-08GO28308

ACI-1532235

ACI-1532236

History

Email Address of Submitting Author

peter.stjohn@nrel.gov

Institution

National Renewable Energy Laboratory

Country

USA

ORCID For Submitting Author

0000-0002-7928-3722

Declaration of Conflict of Interest

No conflict of interest

Version Notes

paper edits

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