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This paper investigates whether multimodal large language models (MLLMs) can leverage Miller indices as a latent representation to reason about crystallographic fracture geometry from visual inputs, evaluating their ability to infer physically valid plane hypotheses and determine when such representation is applicable across materials like ceramics, glass, metals, and concrete.
This paper presents AI CFD Scientist, an open-source AI agent for computational fluid dynamics that autonomously discovers physics corrections using vision-language verification and code modification, outperforming general AI scientists on CFD tasks.