MIND: AI Co-Scientist for Material Research

Geonhee Ahn; Donghyun Lee; Hayoung Doo; Jonggeol Na; Hyunsoo Cho; Sookyung Kim

cs.MAApr 15, 2026

MIND: AI Co-Scientist for Material Research

Authors: Geonhee Ahn, Donghyun Lee, Hayoung Doo, Jonggeol Na, Hyunsoo Cho, Sookyung Kim

Organizations: Institute for Multiscale Matter and Systems (IMMS), Ewha Womans University, Seoul 03760, Republic of Korea

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.

Figures & tables

Figure 1: Overview of automated hypothesis validation process in MIND .

Figure 2: Pre-experimental stage workflow.

Panel of Figure 3 — Figure 3: The interactive user interface to submit hypothesis and visualize results.

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