To test interpolative performance, we used random splitting to allocate 80% of the data to training, 10% to validation, and 10% to testing. While random splits are common in the literature, they primarily assess performance on relatively simple interpolation tasks. However, a major application of property prediction models is to predict properties for molecules and reactions that are very different from those already studied. Therefore, evaluating model performance on more challenging extrapolative tasks is crucial to assess generalizability. While scaffold splitting 52 is a common approach, it is unsuitable for our dataset as most molecules lack assigned scaffolds. Instead, we used a K-Means clustering-based method to create chemically dissimilar training, validation, and test sets. We first generated 2048-bit Morgan fingerprints 1,2 with a radius of 4 for each molecule using RDKit, 27 then applied principal component analysis (PCA) to reduce the dimensionality of these sparse vectors.