Evaluation of GPT-4o and GPT-4o-mini's Vision Capabilities for Compositional Analysis from Dried Solution Drops

Deven B. Dangi, Beni B. Dangi, Oliver Steinbock
12/13/2024
cs.CVcs.AIcs.CL

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Abstract

When microliter drops of salt solutions dry on non-porous surfaces, they form erratic yet characteristic deposit patterns influenced by complex crystallization dynamics and fluid motion. Using OpenAI's image-enabled language models, we analyzed deposits from 12 salts with 200 images per salt and per model. GPT-4o classified 57% of the salts accurately, significantly outperforming random chance and GPT-4o mini. This study underscores the promise of general-use AI tools for reliably identifying salts from their drying patterns.

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Cite this paper

@article{dangi2024evaluation,
  title  = {Evaluation of GPT-4o and GPT-4o-mini's Vision Capabilities for Compositional Analysis from Dried Solution Drops},
  author = {Deven B. Dangi and Beni B. Dangi and Oliver Steinbock},
  year   = {2024},
  eprint = {2412.10587},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2412.10587v2}
}

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