Two general solutions have been devised to increase model accuracy for experimental data. First, data augmentation strategies to mimic experimental noise, preferential orientation, peak broadening, background, or a combination of artifacts in simulated data (Fig. ) can increase accuracy in experimental pattern classification . Data augmentation strategies result in simulated training data that more closely aligned with real-world experimental data, which improves model performance. For example, Lee at al. . General data augmentation procedure to increase accuracy on experimental data from Oviedo et al. After simulating a series of intensities, peaks are scaled and eliminated to simulate orientation and site occupancy effects and shifted to simulate the effects of strain in synthesized samples.