The lattice constants from XRD can also be predicted using other ML techniques such as gradient boosting regression tree (GBR), convolutional neural networks (CNN), and various DiffractGPT (DGPT) models, as shown in Table . The mean absolute errors (MAE) for predicting a, b, and c lattice constants on the test set for GBR are 1.03 Å, 0.99 Å, and 1.27 Å. Similarly, for CNN models, MAEs of 0.28 Å, 0.27 Å, and 0.28 Å are observed, which is a significant improvement compared to GBR. Now, the performance of three types of DiffractGPT models-those with chemical information, with element lists, and with explicit formulas-shows the minimum error for the model with explicit formulas, which is intuitively correct. Specifically, the lowest error in lattice constant predictions was observed for the alattice parameter at 0.17 Å. This value is close to the CNN model predictions. Li et al.