MIND: AI Co-Scientist for Material Research

• URL: http://arxiv.org/abs/2604.13699v1
• Date: Wed, 15 Apr 2026 10:27:01 GMT
• Title: MIND: AI Co-Scientist for Material Research
• Abstract summary: MIND organizes the scientific discovery process into hypothesis refinement, experimentation, and debate-based validation.<n>For experimental verification, the system integrates Machine Learning Interatomic Potentials.<n>The modular design allows additional experimental modules to be integrated, making the framework adaptable to broader scientific research.
• Score: 13.842488789399743
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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: https://github.com/IMMS-Ewha/MIND, and a demonstration video at: https://youtu.be/lqiFe1OQzN4.

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