MIND: AI Co-Scientist for Material Research
Large language models (LLMs) have enabled agentic AI systems for scientific discovery, but most approaches remain limited to textbased reasoning without automated experimental verification. We propose MIND, an LLM-driven framework for automated hypothesis validation in materials research. MIND organizes the scientific discovery process into hypothesis refinement, experimentation, and debate-based validation within a multi-agent pipeline. For experimental verification, the system integrates Machine Learning Interatomic Potentials, particularly SevenNet-Omni, enabling scalable in-silico experiments. We also provide a web-based user interface for automated hypothesis testing. The modular des...
| Authors | Geonhee Ahn, Donghyun Lee, Hayoung Doo et al. |
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| Primary category | cs.MA |
| Published | 15 April 2026 |
| Last updated at source | 15 April 2026 |
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