Can Multimodal LLMs See Materials Clearly? A Multimodal Benchmark on Materials Characterization

2025-09-11

AI Summary

Current multimodal large language models (MLLMs) face dual bottlenecks in materials characterization image understanding: insufficient domain-specific expert knowledge and inadequate complex visual perception. To address this, we introduce MatCha—the first multimodal benchmark tailored to materials science—spanning four key research stages, 21 realistic tasks, and 1,500 expert-level reasoning questions derived from authentic scientific practice. We systematically evaluate mainstream MLLMs using few-shot learning and chain-of-thought prompting. Results reveal a substantial performance gap between state-of-the-art models and human experts, particularly in higher-order reasoning and fine-grained microstructural identification. MatCha fills a critical void in domain-specific multimodal evaluation benchmarks and, for the first time, empirically exposes the adaptive limitations of MLLMs in real-world materials research scenarios. It thus establishes an essential benchmark for guiding model refinement, domain alignment, and rigorous assessment of scientific reasoning capabilities in multimodal AI.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Knowledge Representation and Reasoning: Computational Complexity of Reasoning
• Natural Language Processing: Language Grounding & Multi-modal NLP

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data

Abstract

Materials characterization is fundamental to acquiring materials information, revealing the processing-microstructure-property relationships that guide material design and optimization. While multimodal large language models (MLLMs) have recently shown promise in generative and predictive tasks within materials science, their capacity to understand real-world characterization imaging data remains underexplored. To bridge this gap, we present MatCha, the first benchmark for materials characterization image understanding, comprising 1,500 questions that demand expert-level domain expertise. MatCha encompasses four key stages of materials research comprising 21 distinct tasks, each designed to reflect authentic challenges faced by materials scientists. Our evaluation of state-of-the-art MLLMs on MatCha reveals a significant performance gap compared to human experts. These models exhibit degradation when addressing questions requiring higher-level expertise and sophisticated visual perception. Simple few-shot and chain-of-thought prompting struggle to alleviate these limitations. These findings highlight that existing MLLMs still exhibit limited adaptability to real-world materials characterization scenarios. We hope MatCha will facilitate future research in areas such as new material discovery and autonomous scientific agents. MatCha is available at https://github.com/FreedomIntelligence/MatCha.

Problem

• Assessing MLLMs' understanding of materials characterization imaging data
• Evaluating expert-level image comprehension in materials science applications
• Identifying performance gaps in multimodal models for real-world materials analysis

Innovation

• MatCha benchmark for materials characterization
• Multimodal LLMs evaluation on imaging data
• 1500 expert-level domain expertise questions