performed a similar task for predicting lattice constants and found a mean absolute error (MAE) of 0.48 Å50 and an R 2 of 0.80. Although the datasets for these two works are different, a MAE of 0.17 Å suggests promising results. As larger databases are used for DiffractGPT in the future, the MAE may further decrease. Note that DiffractGPT provides not only lattice constants but also full atomic structure information, such as chemical elements and coordinates. Hence, as a second evaluation, we compare the root mean square distance (RMS-d) between the predicted and target materials in the test set and find that the lowest error is observed for the DGPT model with explicit formulas. The RMS-d of 0.07 Å is comparable to the AtomGPT value of 0.08 Å for the superconductor design task. To illustrate further, we show the predicted lattice constants and volumes for the Diffract-GPT chemical formula + XRD pattern model in Fig. . The color of the dots in the plot represents different crystal lattice types. The cubic, tetragonal, orthorhombic, hexagonal, trigonal, monoclinic, and triclinic systems are represented by blue, green, red, cyan, magenta, purple, and black colors, respectively. The values that lie on the x = y line represent perfect agreement, while points away from it represent outliers. We barely observe outliers from symmetric lattice systems such as cubic materials. Most of the outliers are from the red and purple dots, representing orthorhombic and monoclinic systems. We find the maximum R 2 score of 0.85 (for lattice constant b) and the minimum R 2 of 0.78 for lattice constant a. Now, we present an overview of the usability of the DiffractGPT framework in Fig. .