As discussed in greater detail below, a small number of features (total solids concentration and spin coating speed in particular) were missing from many of the data points (Figure ). These features were excluded in order to maximize the size of the dataset on which the models were trained (Figure ). The top structural representations, ECFP and Mordred descriptors, were chosen for further evaluation with device fabrication features. Although the KRR and GPR models with ECFP structural representations, which used the Tanimoto kernel, performed well, we were not able to evaluate their performance when device fabrication features are included. Although mixing different kernels is possible, it is unclear whether it would be meaningful and how the weights would be learned. The Tanimoto kernel calculates distance based on all 4096 ECFP features while device fabrication feature distances would be calculated one at a time. As such, assigning correct weights would be non-trivial.