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

Abstract

When microliter drops of salt solutions dry on nonporous 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.