Large language models for reticular chemistry

IF 79.8 1区 材料科学 Q1 MATERIALS SCIENCE, MULTIDISCIPLINARY
Zhiling Zheng, Nakul Rampal, Theo Jaffrelot Inizan, Christian Borgs, Jennifer T. Chayes, Omar M. Yaghi
{"title":"Large language models for reticular chemistry","authors":"Zhiling Zheng, Nakul Rampal, Theo Jaffrelot Inizan, Christian Borgs, Jennifer T. Chayes, Omar M. Yaghi","doi":"10.1038/s41578-025-00772-8","DOIUrl":null,"url":null,"abstract":"<p>Reticular chemistry is the science of connecting molecular building units into crystalline extended structures such as metal–organic frameworks and covalent organic frameworks. Large language models (LLMs), a type of generative artificial intelligence system, can augment laboratory research in reticular chemistry by helping scientists to extract knowledge from literature, design materials and collect and interpret experimental data — ultimately accelerating scientific discovery. In this Perspective, we explore the concepts and methods used to apply LLMs in research, including prompt engineering, knowledge and tool augmentation and fine-tuning. We discuss how ‘chemistry-aware’ models can be tailored to specific tasks and integrated into existing practices of reticular chemistry, transforming the traditional ‘make, characterize, use’ protocol driven by empirical knowledge into a discovery cycle based on finding synthesis–structure–property–performance relationships. Furthermore, we explore how modular LLM agents can be integrated into multi-agent laboratory systems, such as self-driving robotic laboratories, to streamline labour-intensive tasks and collaborate with chemists and how LLMs can lower the barriers to applying generative artificial intelligence and data-driven workflows to such challenging research questions as crystallization. This contribution equips both computational and experimental chemists with the insights necessary to harness LLMs for materials discovery in reticular chemistry and, more broadly, materials science.</p>","PeriodicalId":19081,"journal":{"name":"Nature Reviews Materials","volume":"84 1","pages":""},"PeriodicalIF":79.8000,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Nature Reviews Materials","FirstCategoryId":"88","ListUrlMain":"https://doi.org/10.1038/s41578-025-00772-8","RegionNum":1,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MATERIALS SCIENCE, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0

Abstract

Reticular chemistry is the science of connecting molecular building units into crystalline extended structures such as metal–organic frameworks and covalent organic frameworks. Large language models (LLMs), a type of generative artificial intelligence system, can augment laboratory research in reticular chemistry by helping scientists to extract knowledge from literature, design materials and collect and interpret experimental data — ultimately accelerating scientific discovery. In this Perspective, we explore the concepts and methods used to apply LLMs in research, including prompt engineering, knowledge and tool augmentation and fine-tuning. We discuss how ‘chemistry-aware’ models can be tailored to specific tasks and integrated into existing practices of reticular chemistry, transforming the traditional ‘make, characterize, use’ protocol driven by empirical knowledge into a discovery cycle based on finding synthesis–structure–property–performance relationships. Furthermore, we explore how modular LLM agents can be integrated into multi-agent laboratory systems, such as self-driving robotic laboratories, to streamline labour-intensive tasks and collaborate with chemists and how LLMs can lower the barriers to applying generative artificial intelligence and data-driven workflows to such challenging research questions as crystallization. This contribution equips both computational and experimental chemists with the insights necessary to harness LLMs for materials discovery in reticular chemistry and, more broadly, materials science.

Abstract Image

求助全文
约1分钟内获得全文 求助全文
来源期刊
Nature Reviews Materials
Nature Reviews Materials Materials Science-Biomaterials
CiteScore
119.40
自引率
0.40%
发文量
107
期刊介绍: Nature Reviews Materials is an online-only journal that is published weekly. It covers a wide range of scientific disciplines within materials science. The journal includes Reviews, Perspectives, and Comments. Nature Reviews Materials focuses on various aspects of materials science, including the making, measuring, modelling, and manufacturing of materials. It examines the entire process of materials science, from laboratory discovery to the development of functional devices.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信