arXiv:2604.13699 · v2
MIND: AI Co-Scientist for Material Research
摘要
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 design allows additional experimental modules to be integrated, making the framework adaptable to broader scientific workflows. The code is available at: https://github.com/IMMS-Ewha/MIND, and a demonstration video at: https://youtu.be/lqiFe1OQzN4.
作者备注：4 pages, 3 figures. Accepted at ECML PKDD 2026, Demonstration Track. Code: https://github.com/IMMS-Ewha/MIND . Demo video: https://youtu.be/lqiFe1OQzN4
期刊信息：In: Machine Learning and Knowledge Discovery in Databases. Applied Data Science Track, Demo Track and Industrial Track, LNCS 16950, pp. 324-328 (2027)