To train ML models with the dataset, it was important to address the asymmetric size of each morphology class with the majority being AuNR syntheses (379 recipes) and the nanostar syntheses (21 recipes) being the next most common. We first separated the dataset into AuNR recipes and non-rod recipes. AuNR and non-rod recipes were then split with a 98:2 and 6:4 train/test ratio, respectively. This would give a test set with a rod class size similar to the star class size, both with around 7 data points. After the split, training data of non-rod recipes were copied and duplicated by 30. This number was chosen to make each of the non-rod class size similar to the rod class size in the training set. We tested with 8 different seed values for the splits, and the accuracy scores were averaged across 8 seeds.