Until this point we only demonstrated that the data can be successfully correlated. Of course, the aim of any such investigation is to predict rate coefficients for monomers for which no experimental kp data is available. With the good correlation demonstrated in Figure , predictions of rate coefficients should be possible as long as the unknown monomer largely falls into a similar category as the training data, i.e. doesn't feature exotic functional groups or represents a monomer with very different radical stability. Since practically all features in our model are based on calculated features, it is fairly straight forward to include further monomers into the list. As monomers in question we chose ethyl acrylate, propyl acrylate, cyclohexyl acrylate and propyl methacrylate. These monomers are an interesting comparison since it is known that propagation rate coefficients increase generally with the size of the ester side chain for both acrylates and methacrylates, and reference data are available for the corresponding methyl and butyl (meth)acrylates. All ML-predicted k p values are given in Table alongside errors derived from the overall r 2 of the fit in Figure 3c As a first observation of the data in Table , one can see that qualitatively the order of predicted propagation rates in the acrylates is correct. A slight increase in rate for the ethyl to the propyl acrylate is seen, while the cyclohexyl acrylate monomer exhibits the highest rate coefficient. For both the acrylate and methacrylate the correct order of magnitude is predicted (which is, given the large number of monomers used in the correlation from these two families not surprising). Starting from the experimental propagation rate coefficients for methyl and butyl methacrylate, a kp between 323 and 370 would be expected. The value for propyl methacrylate is very close in the range of expectations. Given that size-exclusion chromatography, which is key to experimental kp determinations is commonly associated with an error of up to 20%, this match is exceptionally good, and outperforms any prediction based on ab-initio calculations provided so far. For the acrylates, using the same comparison, a kp between 13130 (methyl acrylate) and 16380 (butyl acrylate) would be expected. Again, the predictions made by our regressions meet this range. For cyclohexyl acrylate, no clear expectation can be given, yet the produced value seems to be a reasonable estimate when compared to other data. It will be interesting to see in the future if these values will be confirmed by experiments. The error limits given in Table are rather large, and disparate towards higher and lower values. This is due to predictions being made on a log scale. The overall error estimate is probably rather conservative, and reflects also the error in all used training data, which of course is not absolute at all. Regardless, we have made our Python script for the prediction available via the github platform, so that other researchers can make their own predictions based on need.