Correlation of substituent properties with the activation energy and energy prediction. Thus far, the high-throughput virtual screening enabled a first computational exploration of the chemical space of silylenes for H2 activation and demonstrated that the reaction energy and activation barrier can be predicted by substituent energy increments. However, the available data set also allows for an analysis of relationships between these energy values and the steric and electronic properties of the silylenes and their substituents. Given that the bag of substituents does not allow to predict energies of substituents beyond the used data set, such a correlation with physicochemical features would remove this limitation and facilitate extrapolation to so far untested substituents, which will be helpful to accelerate the exploration of potential main-group element catalysts in the future. To describe the silylenes and their substituents and to guarantee interpretability of the results we selected a descriptor-based parametrization of the molecules including steric and electronic properties (Table ). Such descriptors have been used for the featurization of ligands in transition metal catalysts and successfully applied for the prediction of optimal ligand structures to improve the catalytic performance. A total of 98 features were obtained from DFT calculations, population analyses and conceptual DFT methods to characterize each silylene and the respective substituents (see the Supporting Information for details). The Pearson correlation coefficients were calculated to examine the correlation of the properties with the activation barrier and reaction energy (Figure ). The HOMO-LUMO gap is often used in literature to explain the reactivity of carbenes towards small molecule activation. Indeed, this descriptor was found to strongly correlate (ρ = 0.80) with the activation barrier, although the hardness of the silicon atom (= fundamental gap, IP-EA) showed a stronger correlation (ρ = 0.82) and thus would be better suited as a single descriptor for estimating the H2 activation potential of silylenes. Despite the strong correlation of both features with the barrier, using these features alone for univariate linear regression turned out to be insufficient for making reliable predictions of the activation energy. For example, regression of the HOMO-LUMO gap with the barrier results in a mean absolute error (MAE) of 25.8 kJ/mol (0.48) (R 2 = 0.64, Figure ). This limitation may be explained by the fact that the HOMO and LUMO orbitals are usually not entirely localized at the silicon center but often delocalized across the substituents. Another frequently discussed feature is the R1-Si-R2 angle, which however exhibits no correlation with the barrier according to our data (RER angle: R 2 = 0.04, MAE = 42.70 kJ/mol (0.79)). Consequently, this feature is not suitable for predicting barriers in acyclic silylenes, but may help to explain the higher barriers observed in cyclic silylenes compared to acyclic ones. To obtain a more reliable prediction of the H2 activation barrier based on the silylenes properties, multivariate linear regression with 7 linear independent descriptors was performed, which enabled an improved prediction (MAEtest = 12.17 kJ/mol (0.22), Figure ). Within this model, the HOMO-LUMO gap, the local softness s -of the silicon atom in terms of the removal of an electron and the occupation of the lone pair at the silicon center are the most important features. To both, simplify and further improve our model, we probed including a categorical variable for the elements in α-position to the silicon center in addition to the physicochemical descriptors, since we had observed a strong relationship between these elements and the calculated energy increments of the substituents (Figure ). Similar to the bag of substituents, the categorical variable of elements in α-position was transformed into a matrix with all unique elements (Si, B, P, C, S, N, O) and the matrix elements describing how often this element is present in α-position (0, 1, 2) in the respective silylene. This approach again enabled the calculation of energy increments specific to these elements (Table ). To our delight, this simple approach allowed for a fast estimation of the activation barrier, with a mean average error of only approx. 11 kJ/mol (R 2 (test) = 0.95; see Figure ). It is surprising that this model performs even better than the first model based on steric and electronic descriptors and underlines the importance of the nature of the α-element for the silylene properties. This simple model allows the rapid estimation of the activation barrier for any silylene based on the elements in the α-position and can therefore be easily calculated in head. For example, a barrier of about 64 k/mol (±11 kJ/mol) is expected for a diarylsilylene. Combining the matrix of the elements in α-position with five linearly independent property descriptors obtained from DFT calculations finally led to a model with ridge regression that predicts the barrier with a mean absolute error lower than 9 kJ/mol (MAEtest = 8.95 kJ/mol) and thus allows for a reliable prediction (Figure ). Within this model, the value of the R-Si-R angle, the softness of one of the substituents towards removal of an electron and the electrophilicity of the silicon atom were found to be the most important property features. Using the same approach for modelling the energy of the activation processes, also allowed for an accurate prediction of ΔGH2 with a MAE of only approx. 8 kJ/mol (Figure ).