The alternative solution we considered is the featurization scheme in (4), which essentially replaces zero values with very small constant values. This constant was chosen considering the range of precursor concentrations, the minimum being 1×10 -8 M for seed precursors after converting to the final concentration in the final seed-growth solution mixture. When trained for the whole dataset, using sufficiently large C, either the model prediction simply deviates from the interpretable model in (6), or the coefficients of the precursors that were not used in any data point would end up very small during the training process. This essentially means that HAuCl4, which is nonzero in all of the data points, would have a considerably large coefficient than any of the other precursors. This is counter-intuitive, as we expect different mechanisms to take place depending on the precursors that are present.