To simulate crowded spectra, we developed an approach to synthetically mix two known clusters. First, a primary and secondary cluster were chosen at random from the full data set. The m/z of the secondary distribution was shifted to be within the same encoding window as the primary cluster. The intensity of the secondary distribution was normalized randomly to between 5% and 20% of the primary distribution, so that a clear charge state assignment was still present. After testing different amounts of mixed data, we trained against data with 40% synthetic double mixes. Additional alternative training strategies were tested that did not improve the model performance, and they are briefly described in the Supplemental Methods (SM3).