Coming from this rather sobering result, we then extended the feature set of the regressions, taking all available physical properties into account, hence molecular weight, dipolar moment, melting and boiling point and Gibbs free energy values. The outcome of such prediction is given in Figure . As can be seen, no correlation is obvious and the plot seems almost random. No obvious correlation can be seen. Hence, we moved forward by adding further information to the feature set. To achieve this aim, we first broke the number of datapoints down, and isolated the (meth)acrylates from the list. Then, we introduced a binary differentiation for acrylates (one) and methacrylates (zero). This alone lead to a reasonable representation of data, showing that the calculated physical properties of monomers have a positive effect, and are aiding in the prediction of rate coefficients. However, several difficulties remained. For example, hydroxy ethyl acrylate was not appropriately predicted. Also branched monomers, while improved compared to the state-of-the-art representation in Figure , still showed significant deviations. To solve these issues, the feature set was extended by two parameters. One parameter described the inductive effect of the side chain, the other quantifies H-bonding between monomers. For both effects, literature was screened, and property tables provided by high level quantum calculation could be used. It should be hereby noted that H-bonding is not easy to quantify, and only the presence of major functional groups was accounted for. Despite the shortcomings of this process, a very good prediction is obtained in this way. Figure depicts the case fitting of acrylates and methacrylates without inclusion of strongly Hbonding monomers. It should be noted that both LASSO and Ridge regressions yield similarly reasonable results, while conventional linear regressions perform significantly less well. Nonetheless, the r 2 value of the plot shown in Figure is 0.991, underpinning its overall high quality. Statistical analysis of the LASSO regression shows that either the melting point or the boiling point can be used, both information is not required since they display high collinearity. The same was true for surface tension data and refractive index data. Both features were practically redundant when polarizability was used. All other features do contribute to the results.