MIND: AI Co-Scientist for Material Research
For materials scientists, MIND provides a modular, automated pipeline for hypothesis testing, reducing manual experimentation effort.
MIND is an LLM-driven framework for automated hypothesis validation in materials research, integrating multi-agent debate and machine learning interatomic potentials for in-silico experiments. The system enables scalable, automated experimental verification without human intervention.
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.