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.

翻译：大语言模型（LLMs）已使基于智能体的人工智能系统应用于科学发现成为可能，但多数方法仍局限于基于文本的推理，缺乏自动化的实验验证。我们提出MIND——一个由大语言模型驱动的材料研究自动化假设验证框架。MIND将科学发现过程组织为假设精炼、实验以及基于辩论的验证，并在多智能体流水线中执行。在实验验证方面，该系统集成了机器学习原子间势能，特别是SevenNet-Omni，从而支持可扩展的计算机模拟实验。我们还提供了一个基于网页的用户界面，用于自动化的假设测试。模块化设计允许集成额外的实验模块，使该框架可适应更广泛的科学工作流。代码见：https://github.com/IMMS-Ewha/MIND，演示视频见：https://youtu.be/lqiFe1OQzN4。