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

15 November 2019, Version 2
This content is a preprint and has not undergone peer review at the time of posting.


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 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.


machine learning
Bond Dissociation Enthalpies


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