DARWIN 1.5: Large Language Models as Materials Science Adapted Learners
Abstract
Materials discovery and design aim to find compositions and structures with desirable properties over highly complex and diverse physical spaces. Traditional solutions, such as high-throughput simulations or machine learning, often rely on complex descriptors, which hinder generalizability and transferability across different material systems. Moreover, These descriptors may inadequately represent macro-scale material properties, which are influenced by structural imperfections and compositional variations in real-world samples, thus limiting their practical applicability. To address these challenges, we propose DARWIN 1.5, the largest open-source large language model tailored for materials science. By leveraging natural language as input, DARWIN eliminates the need for task-specific descriptors and enables a flexible, unified approach to material property prediction and discovery. Our approach integrates 6M material domain papers and 21 experimental datasets from 49,256 materials across modalities while enabling cross-task knowledge transfer. The enhanced model achieves up to 59.1% improvement in prediction accuracy over the base LLaMA-7B architecture and outperforms SOTA machine learning approaches across 8 materials design tasks. These results establish LLMs as a promising foundation for developing versatile and scalable models in materials science.
Cite
@article{arxiv.2412.11970,
title = {DARWIN 1.5: Large Language Models as Materials Science Adapted Learners},
author = {Tong Xie and Yuwei Wan and Yixuan Liu and Yuchen Zeng and Shaozhou Wang and Wenjie Zhang and Clara Grazian and Chunyu Kit and Wanli Ouyang and Dongzhan Zhou and Bram Hoex},
journal= {arXiv preprint arXiv:2412.11970},
year = {2025}
}
Comments
This version of the manuscript was posted prematurely and contains inaccuracies that could mislead readers. The authors are preparing a significantly revised version with substantial methodological and experimental updates, and prefer to avoid confusion with earlier postings. We apologize for any inconvenience and thank the community for their understanding