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原文摘要

Machine learning for powder diffraction (PXRD) symmetry classification is being trained at scale, yet the field lacks a common evaluation standard: papers report results on different subsets of experimental archives, with different target taxonomies, preprocessing, and metrics, and published models are rarely released in a form that permits rescoring. We present InformedXRD, an evaluation framework with three parts: (i) a machine-readable mapping from 230 space groups to 99 extinction groups, the finest classes powder diffraction can identify from systematic absences alone; (ii) a benchmark-construction method whose inclusion rules are specified as code and reproduce from a frozen snapshot, instantiated as RRUFF-473, RRUFF-325, and, as a small example, opXRD-98; (iii) a reporting protocol with prior-only frequency baselines, a label-permutation test, a hierarchy-aware error metric on the condensed translationengleiche subgroup graph, and stratified evaluation. We build on the mapping, RRUFF benchmarks, subgroup graph, and four ViT checkpoints released in our earlier work (Baggett et al., 2026). Using these checkpoints, a 1D residual CNN, and six architectures trained under the XRDBench recipe (Cao et al., 2025), we show that evaluation design changes what conclusions are justified: paired tests point in different directions at Top-1 and Top-5; paired Top-k tests do not separate the three leading XRDBench models, while hierarchy-aware analysis describes error distance and direction across all six; an input-blind frequency predictor matches the models on aggregate Top-k, yet a label-permutation test still detects pattern-specific evidence; and stratified analysis exposes a label-entropy confound that aggregate accuracy hides. All mapping tables, benchmark code, scripts, and checkpoints are released as shared infrastructure for PXRD-ML evaluation.