Large Language Models in Chemistry and Materials Science: A Comprehensive Survey

中文 / Bilingual · English version

Jinshi Li, Chengchun Liu, Chengrui Wei, Yimi Wang, Boxuan Zhao, Zhiyuan Yan, Wendi Cai, Wenchao Wu, Shuangyan Tian, Zifu Wang, Weida Wang, Shufei Zhang, Jie Zhu, and Fanyang Mo

Cite This: ChemRxiv (2026). DOI: 10.26434/chemrxiv.15009184/v1. Posted September 21, 2026.

ABSTRACT: Large language models (LLMs) are expanding the scope of artificial intelligence in chemistry and materials science by connecting scientific knowledge, heterogeneous data, computational tools, and experimental workflows. This article presents a comprehensive survey of the foundations, representations, methods, applications, and evaluation of LLM-centered scientific systems. We develop a non-exclusive representation-by-mechanism taxonomy linking text, molecular and crystal structures, spectra, images, and numerical properties with model adaptation, cross-representation grounding, external knowledge and tools, and workflow coordination. Within this framework, we synthesize prompting, domain pre-training and fine-tuning, preference alignment and verifier-driven reinforcement learning, multimodal integration, and retrieval- and agent-based approaches. Applications span literature understanding, property prediction, molecular and materials design, synthesis planning, characterization, hypothesis generation, and closed-loop discovery. By linking method choices to input information, task definitions, and validation conditions, the framework clarifies which reported results can be compared and what scientific claims they support. We organize representative datasets and benchmarks and introduce an evaluation matrix connecting task performance with information fidelity, uncertainty, chemical and physical validity, and workflow reliability. Detailed supplementary cases document the settings and evidence underlying key comparisons. Finally, we identify research priorities in integrated scientific representations, efficient adaptation, reusable knowledge and tool interfaces, and verifiable human-machine collaboration. By linking methodological developments to scientific objectives, this survey provides a conceptual map and practical guidance for developing and assessing LLM-enabled chemical and materials discovery.

Note: This is a ChemRxiv preprint. Preprint: ChemRxiv.