Evaluation of GPT-4o and GPT-4o-mini's Vision Capabilities for Compositional Analysis from Dried Solution Drops
This work addresses the need for reliable salt identification in chemistry or materials science, but it is incremental as it applies existing AI models to a new dataset.
The study tackled the problem of identifying salts from their drying patterns using AI vision models, finding that GPT-4o achieved 57% accuracy, significantly outperforming random chance and GPT-4o-mini.
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.