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arXiv · 2506.13051

Stress-Testing Multimodal Foundation Models for Crystallographic Reasoning

Can Polat·Hasan Kurban·Erchin Serpedin·Mustafa Kurban
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Abstract

Evaluating foundation models for crystallographic reasoning requires benchmarks that isolate generalization behavior while enforcing physical constraints. This work introduces a multiscale multicrystal dataset with two physically grounded evaluation protocols to stress-test multimodal generative models. The Spatial-Exclusion benchmark withholds all supercells of a given radius from a diverse dataset, enabling controlled assessments of spatial interpolation and extrapolation. The Compositional-Exclusion benchmark omits all samples of a specific chemical composition, probing generalization across stoichiometries. Nine vision--language foundation models are prompted with crystallographic images and textual context to generate structural annotations. Responses are evaluated via (i) relative errors in lattice parameters and density, (ii) a physics-consistency index penalizing volumetric violations, and (iii) a hallucination score capturing geometric outliers and invalid space-group predictions. These benchmarks establish a reproducible, physically informed framework for assessing generalization, consistency, and reliability in large-scale multimodal models. Dataset and code are available at https://github.com/KurbanIntelligenceLab/StressTestingMMFMinCR.

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Can Polat, Hasan Kurban, Erchin Serpedin, Mustafa Kurban. 2025-06-16. Stress-Testing Multimodal Foundation Models for Crystallographic Reasoning. https://arxiv.org/abs/2506.13051

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