SCALAR: Cross-Scale Benchmark of Nanoparticle Structures for Quantifying Hallucination, Consistency, and Reasoning in Materials Foundation Models

SCALAR (Structural Consistency And Logic Across Regimes) is a cross-scale benchmark of ≈100,000 nanoparticle structures (25–18,123 atoms across 83 predominantly hydride materials, carving radii 10–30 Å) designed to evaluate structural hallucination, cross-scale consistency, and physical reasoning in materials foundation models. Each structure is a spherical truncation of a DFT-relaxed, experimentally validated crystal lattice; the benchmark treats radius as a controlled scaling knob and provides leakage-free in-distribution and out-of-distribution splits over both radius and SO(3) rotation. This archive contains the seed inputs for the benchmark: the 83 primitive unit cell CIFs and per-(material, radius) base XYZ structures. The full benchmark (with rotation augmentation) is reproduced locally by running the generation pipeline shipped in the GitHub repository. Paper: "SCALAR: Quantifying Structural Hallucination, Consistency, and Reasoning Gaps in Materials Foundation Models" (submitted to Digital Discovery). Code & generation pipeline: github.com/KurbanIntelligenceLab/SCALAR (MIT license). To reproduce the full benchmark from this archive: python -m create_scalar.create_scalar --raw-data scalar_raw.zip --output scalar Version 1.1 (correction). Replaces scalar_raw/NaRhH3/NaRhH3_R30.xyz, which in version 1.0 contained 11,917 atoms — identical to the R = 29 Å structure and the only structure in the archive whose atom count did not increase with radius. Its effective density was 10.2% below the value stable across R = 10–29 Å for this material. The corrected structure, regenerated with create_scalar/carve.py, contains 13,249 atoms at the expected density. All other 1,742 structures are byte-identical to version 1.0.