The interplay between homolytic bond dissociation energy (BDE) and heterolytic bond dissociation energies is crucial for understanding reactivity. Acidity (pK a ) describes the heterolytic bond dissociation energy involving proton dissociation, while hydricity describes the heterolytic bond dissociation energy involving hydride dissociation (see Figure ). Although researchers have extensively studied pK a and BDE, hydricity has received less attention. For example, Cernak et al. grouped mechanistically distinct innate insertion and H-abstraction reactions, arguing that both follow the same pattern where carbocation/radical stability is 3 • > 2 • > 1 • . Hydride transfer reactions have diverse applications, such as H 2 synthesis , CO 2 reduction , olefin isomerization , and various C-H functionalizations . Understanding the thermodynamic hydricity or conjugate hydride affinity (HA) is critical to predicting substrate stability and reactivity in these reactions. Hydricity refers to the free energy change associated with the heterolytic bond dissociation between a hydride (H -) and a substrate, while hydride affinity is the reverse process, C -H ⇌ C + + H -. Molecules with high hydricity values are weak hydride donors (strong acceptors), and those with low hydricity values are strong hydride donors (weak acceptors). Many studies have explored metal hydrides , which can be expensive, toxic, and difficult to synthesize. Consequently, organic hydride donors present an interesting alternative, yet their experimental hydricities are underexplored. Parker and co-workers first published experimental hydricities of cations , radicals , and quinones , with few subsequent studies . Measuring experimental hydricities is challenging, unlike pK a measurements. The "potential pK a " method, which uses thermochemical cycles alongside pK a and electrode potential measurements, exemplifies this complexity. This method's strenuous nature and an accuracy margin of up to ±3 kcal/mol likely contribute to the lack of experimental data for organic hydride donors compared to metal hydrides. Computational studies have attempted to fill this gap . Recently, Yeo et al. benchmarked various density functionals, basis sets, and solvation models against experimental hydricities in acetonitrile (MeCN) and dimethyl sulfoxide (DMSO). While they achieved an RMSE of 3.74 kcal/mol in MeCN, their RMSE in DMSO was 9.32 kcal/mol, attributed to the conformational flexibility of substituted dihydro-tricyclic heterocycles. Excluding these outliers improved the RMSE to 6.87 kcal/mol but left too few compounds to rank the computational methods robustly. To our knowledge, no one has used QM computed or ML predicted hydricities to predict reaction sites. We developed HAlator, a QM-based workflow for automatically computing C-H hydricities in DMSO to address the challenges mentioned. These computed hydricities generate training data for a machine learning (ML) model using LightGBM . The QM-based workflow and the ML model are freely available under the MIT license.