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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2604.13699 |
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| _version_ | 1866910131202031616 |
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| author | Ahn, Geonhee Lee, Donghyun Doo, Hayoung Na, Jonggeol Cho, Hyunsoo Kim, Sookyung |
| author_facet | Ahn, Geonhee Lee, Donghyun Doo, Hayoung Na, Jonggeol Cho, Hyunsoo Kim, Sookyung |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_13699 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | MIND: AI Co-Scientist for Material Research Ahn, Geonhee Lee, Donghyun Doo, Hayoung Na, Jonggeol Cho, Hyunsoo Kim, Sookyung Multiagent Systems Artificial Intelligence Computational Engineering, Finance, and Science 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. |
| title | MIND: AI Co-Scientist for Material Research |
| topic | Multiagent Systems Artificial Intelligence Computational Engineering, Finance, and Science |
| url | https://arxiv.org/abs/2604.13699 |