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arXiv · 2412.10587

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
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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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Deven B. Dangi, Beni B. Dangi, Oliver Steinbock. 2024-12-13. Evaluation of GPT-4o and GPT-4o-mini's Vision Capabilities for Compositional Analysis from Dried Solution Drops. https://arxiv.org/abs/2412.10587

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