Validation of ML models in catalysis is critical to ensure their reliability and accuracy. This is typically done through cross-validation techniques, where the data is split into training and validation sets multiple times to assess model performance. Additionally, experimental validation is often employed, where predictions made by the model are tested in laboratory settings to confirm their accuracy.
