@inproceedings{lai-etal-2025-multimodal,
title = "Can Multimodal {LLM}s See Materials Clearly? A Multimodal Benchmark on Materials Characterization",
author = "Lai, Zhengzhao and
Zheng, Youbin and
Cai, Zhenyang and
Lyu, Haonan and
Yang, Jingpu and
Liang, Hong-Qing and
Hu, Yan and
Wang, Benyou",
editor = "Christodoulopoulos, Christos and
Chakraborty, Tanmoy and
Rose, Carolyn and
Peng, Violet",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2025",
month = nov,
year = "2025",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-emnlp.235/",
doi = "10.18653/v1/2025.findings-emnlp.235",
pages = "4384--4404",
ISBN = "979-8-89176-335-7",
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 $\textbf{MatCha}$, 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."
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<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.</abstract>
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%0 Conference Proceedings
%T Can Multimodal LLMs See Materials Clearly? A Multimodal Benchmark on Materials Characterization
%A Lai, Zhengzhao
%A Zheng, Youbin
%A Cai, Zhenyang
%A Lyu, Haonan
%A Yang, Jingpu
%A Liang, Hong-Qing
%A Hu, Yan
%A Wang, Benyou
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Findings of the Association for Computational Linguistics: EMNLP 2025
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-335-7
%F lai-etal-2025-multimodal
%X 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.
%R 10.18653/v1/2025.findings-emnlp.235
%U https://aclanthology.org/2025.findings-emnlp.235/
%U https://doi.org/10.18653/v1/2025.findings-emnlp.235
%P 4384-4404
Markdown (Informal)
[Can Multimodal LLMs See Materials Clearly? A Multimodal Benchmark on Materials Characterization](https://aclanthology.org/2025.findings-emnlp.235/) (Lai et al., Findings 2025)
ACL
- Zhengzhao Lai, Youbin Zheng, Zhenyang Cai, Haonan Lyu, Jingpu Yang, Hong-Qing Liang, Yan Hu, and Benyou Wang. 2025. Can Multimodal LLMs See Materials Clearly? A Multimodal Benchmark on Materials Characterization. In Findings of the Association for Computational Linguistics: EMNLP 2025, pages 4384–4404, Suzhou, China. Association for Computational Linguistics.