Authors: Mingjun Hu | Chenglong Ma | Wei Li | Wanghan Xu | Jiamin Wu | Jucheng Hu | Tianbin Li | Guohang Zhuang | Jiaqi Liu | Yingzhou Lu | Ying Chen | Chaoyang Zhang | Cheng Tan | Ying Jie | Guo–Cheng Wu | Shuo Gao | Pengcheng Chen | Jiashi Lin | Haitao Wu | Lulu Chen | Fengxiang Wang | Yuanyuan Zhang | Xiangyu Zhao | Feilong Tang | Encheng Su | Jing Ning | Xinyao Liu | Yu Du | Changkai Ji | Pengfei Jiang | Cheng Tang | Ziyan Huang | Jiyao Liu | Jiaqi Wei | Yue-Jin Yang | Xiang Zhang | Guangshuai Wang | Yang Yue | Huihui Xu | Ziyang Chen | Yizhou Wang | Chen Tang | Jianyu Wu | Yuchen Ren | Siyuan Yan | Zhonghua Wang | Zhongxing Xu | Shiyan Su | Shangquan Sun | Runkai Zhao | Zhisheng Zhang | Dingkang Yang | Jinjie Wei | Jiaqi Wang | Jin-Sen Xu | Jiangtao Yan | Wenhao Tang | Hongze Zhu | Yu Liu | Fudi Wang | Yiqing Shen | Yuanfeng Ji | Yanzhou Su | Tong Xie | Hongming Shan | Chun-Mei Feng | Zhi Bin Hou | Diping Song | Lihao Liu | Yanyan Huang | Lequan Yu | Bin Fu | Shujun Wang | X.L. Li | Xiaowei Hu | Yun Gu | Ben Fei | Benyou Wang | Y. Cao | Minjie Shen | Jie Xu | Haodong Duan | Yan Fang | Hongxia Hao | Jielan Li | Jiajun Du | Yanbo Wang | Imran Razzak | Zhongying Deng | Chi Zhang | Lijun Wu | Conghui He | Lu Zhaohui | Jinhai Huang | Wenqi Shao | Yihao Liu | Siqi Luo | Yi Xin | Xiaohong Liu | Fenghua Ling
Source: ArXiv.org (preprint, 2025-08-28)
Field: Materials Science — Materials Chemistry

A Survey of Scientific Large Language Models: From Data Foundations to Agent Frontiers Scientific Large Language Models (Sci-LLMs) are transforming how knowledge is represented, integrated, and applied in scientific research, yet their progress is shaped by the complex nature of scientific data. This survey presents a comprehensive, data-centric synthesis that reframes the development of Sci-LLMs as a co-evolution between models and their underlying data substrate. We formulate a unified taxonomy of scientific data and a hierarchical model of scientific knowledge, emphasizing the multimodal, cross-scale, and domain-specific challenges that differentiate scientific corpora from general natural language processing datasets. We systematically review recent Sci-LLMs, from general-purpose foundations to specialized models across diverse scientific disciplines, alongside an extensive analysis of over 270 pre-/post-training datasets, showing why Sci-LLMs pose distinct demands -- heterogeneous, multi-scale, uncertainty-laden corpora that require representations preserving domain invariance and enabling cross-modal reasoning. On evaluation, we examine over 190 benchmark datasets and trace a shift from static exams toward process- and discovery-oriented assessments with advanced evaluation protocols. These data-centric analyses highlight persistent issues in scientific data development and discuss emerging solutions involving semi-automated annotation pipelines and expert validation. Finally, we outline a paradigm shift toward closed-loop systems where autonomous agents based on Sci-LLMs actively experiment, validate, and contribute to a living, evolving knowledge base. Collectively, this work provides a roadmap for building trustworthy, continually evolving artificial intelligence (AI) systems that function as a true partner in accelerating scientific discovery. Domain (mathematical analysis) ; Sociology of scientific knowledge ; Function (biology) ; Scientific modelling ; Scientific literature ; Benchmark (surveying) ; Taxonomy (biology) ; Scientific discovery Machine Learning in Materials Science ; Topic Modeling ; Artificial Intelligence in Healthcare and Education