Datasets:
| license: mit | |
| task_categories: | |
| - image-text-to-text | |
| tags: | |
| - multimodal | |
| - materials-science | |
| - question-answering | |
| # MatQnA: A Benchmark Dataset for Multi-modal Large Language Models in Materials Characterization and Analysis | |
| This repository hosts the MatQnA dataset, a multi-modal benchmark dataset presented in the paper [MatQnA: A Benchmark Dataset for Multi-modal Large Language Models in Materials Characterization and Analysis](https://huggingface.co/papers/2509.11335). | |
| MatQnA is specifically designed to evaluate the capabilities of AI models in the specialized field of materials characterization and analysis. It includes data from ten mainstream characterization methods, such as X-ray Photoelectron Spectroscopy (XPS), X-ray Diffraction (XRD), Scanning Electron Microscopy (SEM), and Transmission Electron Microscopy (TEM). | |
| The dataset comprises high-quality question-answer pairs, incorporating both multiple-choice and subjective questions, developed using a hybrid approach combining LLMs with human-in-the-loop validation. It serves as a crucial resource for systematically validating and advancing multi-modal AI models in scientific research scenarios related to materials. |