MIND：面向材料研究的 AI 科学家框架

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Abstract: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: this https URL, and a demonstration video at: this https URL.

Comments:	4 pages, 3 figures. Accepted at ECML PKDD 2026, Demonstration Track. Code: this https URL . Demo video: this https URL
Subjects:	Multiagent Systems (cs.MA); Artificial Intelligence (cs.AI); Computational Engineering, Finance, and Science (cs.CE)
Cite as:	arXiv:2604.13699 [cs.MA]
	(or arXiv:2604.13699v2 [cs.MA] for this version)
	https://doi.org/10.48550/arXiv.2604.13699 arXiv-issued DOI via DataCite
Journal reference:	In: Machine Learning and Knowledge Discovery in Databases. Applied Data Science Track, Demo Track and Industrial Track, LNCS 16950, pp. 324-328 (2027)
Related DOI:	https://doi.org/10.1007/978-3-032-37685-5_27 DOI(s) linking to related resources

Submission history

From: Dong Hyun Lee [view email]
[v1] Wed, 15 Apr 2026 10:27:01 UTC (2,028 KB)
[v2] Wed, 4 Nov 2026 20:55:55 UTC (2,028 KB)