Experimental benchmark evaluates BDE prediction methods, showing machine learning can improve accuracy in small molecules.
ExpBDE54 is a benchmark dataset of experimental homolytic bond-dissociation enthalpies (BDEs) for 54 small molecules. Single-point energies were computed using density-functional theory, semiempirical methods, and neural network potentials; linear regression corrections were applied to capture enthalpic effects. g-xTB//GFN2-xTB and OMol25's eSEN Conserving Small define the Pareto frontier, yielding root-mean-square errors of 4.7 and 3.6 kcal⋅mol¯¹. These results demonstrate that suitably corrected semiempirical and machine-learning approaches can enable rapid, accurate BDE predictions.
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Vandezande et al. (2025) studied this question.
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