Artursson et al . demonstrated that select wavelet transforms could improve model (partial least squares) prediction accuracy on a binary phase fraction regression problem while also significantly reducing the number of data points and experimental noise. They also noted that, among others, the Fourier transform and Savitzky-Golay filter improved prediction accuracy. For small datasets (i.e., not applicable to large datasets such as ICSD), Karstang and Eastgate 99 have reported a method to reduce an XRD pattern to only the angles that distinguish samples. Such an approach greatly reduces the input data size and maximizes information density. Preprocessing methods can be particularly impactful for CNN performance. Szymanski et al. recently reported that training with the Fourier transform of an XRD pattern can increase a CNN's ability to differentiate between subtle variations in low-intensity peaks, as would be expected with varying site occupancy and order. This creates a "virtual pair distribution function (PDF)" of the XRD data, which highlights the local arrangement of atoms. The utility of using CNNs to predict crystallographic information from PDF data has been explored in the literature 101,102 , validating the approach of preprocessing XRD data via the Fourier transform. The choice of input representation used (i.e., the original pattern or a transformation of the pattern) impacts which features are represented most strongly and is an important application-specific decision.