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Using synthetic XRD data for a DL approach is inevitable due to the lack of real\u2010world XRD data. There are two main challenges when conducting a DL\u2010based XRD analysis: generating realistic XRD data including all possible perturbations, such as peak shift, broadening, texture, and noisy background, and generalizing the DL model applicability to all ICSD entries. To address both the perturbation and generalizability issues, a large\u2010scale computation is required because it would be infeasible with typical lab\u2010scale computation. Cloud computing infrastructures are leveraged for parallel computations and to obtain symmetry classification test accuracies of 98.95%, 97.18%, and 96.03% for the crystal system, extinction group, and space group, respectively. A stricter individual compound\u2010based train and test dataset\u2010splitting scheme also produces reasonable test accuracies of 92.25%, 87.34%, and 84.39%, which are still state\u2010of\u2010the\u2010art records. Crucially, the DL model trained on synthetic data is assessed using real\u2010world experimental XRD datasets to ensure its practical applicability. 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