Data quality and generalizability also affect model performance on experimental data, which typically includes artifacts and noise. Model performance decreases significantly when classifying experimental data with models exclusively trained on simulated data. Salgado et al. observed a significant decrease in model space group prediction accuracy down to 12% and 22% for CNN and MLP architectures trained with synthetic data. These models were evaluated using data from the RRUFF 78 experimental database. Vecsei et al. similarly reported a decrease in CNN crystal system prediction accuracy from 85% (Table ) 56% when evaluated with the RRUFF database and similar reductions in accuracy for space group classification.