Under Construction
Recent advancements in large language models (LLMs) have revolutionized research discovery across various scientific disciplines, including materials science. The discovery of novel materials, particularly crystal materials, is essential for achieving sustainable development goals (SDGs), as they drive breakthroughs in climate change mitigation, clean and affordable energy, and the promotion of industrial innovation. However, unlocking the full potential of LLMs in materials research remains challenging due to the lack of high quality, diverse, and instruction-based datasets. Such datasets are crucial for guiding these models in understanding and predicting the structure, property, and function of materials across various tasks. To address this limitation, we introduce Mat-Instruction, a large-scale inorganic material instruction dataset, specifically designed to unlock the potential of LLMs in materials science. Extensive experiments on fine-tuning LLaMA with our Mat-Instruction dataset demonstrate its effectiveness in advancing progress for materials science.
- ✅ Release the main code and dataset
- Reconstruct the code structure and release the eval pipeline
- Add Pretrained Models
If you find this work useful in your research, please consider citing:
@inproceedings{ijcai2025p1089,
title = {Mat-Instructions: A Large-Scale Inorganic Material Instruction Dataset for Large Language Models},
author = {Liu, Ke and Gao, Shangde and Fu, Yichao and Wu, Xiaoliang and Tong, Shuo and Rajan, Ajitha},
booktitle = {Proceedings of the Thirty-Fourth International Joint Conference on
Artificial Intelligence, {IJCAI-25}},
publisher = {International Joint Conferences on Artificial Intelligence Organization},
editor = {James Kwok},
pages = {9799--9807},
year = {2025},
month = {8},
note = {AI and Social Good},
doi = {10.24963/ijcai.2025/1089},
url = {https://doi.org/10.24963/ijcai.2025/1089},
}