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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, 19:47 and posted on 15.11.2019, 02:00 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

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