材料发现的基础模型——现状和未来方向

IF 13.1 1区 材料科学 Q1 CHEMISTRY, PHYSICAL npj Computational Materials Pub Date : 2025-03-06 DOI:10.1038/s41524-025-01538-0
Edward O. Pyzer-Knapp, Matteo Manica, Peter Staar, Lucas Morin, Patrick Ruch, Teodoro Laino, John R. Smith, Alessandro Curioni
{"title":"材料发现的基础模型——现状和未来方向","authors":"Edward O. Pyzer-Knapp, Matteo Manica, Peter Staar, Lucas Morin, Patrick Ruch, Teodoro Laino, John R. Smith, Alessandro Curioni","doi":"10.1038/s41524-025-01538-0","DOIUrl":null,"url":null,"abstract":"<p>Large language models, commonly known as LLMs, are showing promise in tacking some of the most complex tasks in AI. In this perspective, we review the wider field of foundation models—of which LLMs are a component—and their application to the field of materials discovery. In addition to the current state of the art—including applications to property prediction, synthesis planning and molecular generation—we also take a look to the future, and posit how new methods of data capture, and indeed modalities of data, will influence the direction of this emerging field.</p>","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"212 1","pages":""},"PeriodicalIF":13.1000,"publicationDate":"2025-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Foundation models for materials discovery – current state and future directions\",\"authors\":\"Edward O. Pyzer-Knapp, Matteo Manica, Peter Staar, Lucas Morin, Patrick Ruch, Teodoro Laino, John R. Smith, Alessandro Curioni\",\"doi\":\"10.1038/s41524-025-01538-0\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>Large language models, commonly known as LLMs, are showing promise in tacking some of the most complex tasks in AI. In this perspective, we review the wider field of foundation models—of which LLMs are a component—and their application to the field of materials discovery. In addition to the current state of the art—including applications to property prediction, synthesis planning and molecular generation—we also take a look to the future, and posit how new methods of data capture, and indeed modalities of data, will influence the direction of this emerging field.</p>\",\"PeriodicalId\":19342,\"journal\":{\"name\":\"npj Computational Materials\",\"volume\":\"212 1\",\"pages\":\"\"},\"PeriodicalIF\":13.1000,\"publicationDate\":\"2025-03-06\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"npj Computational Materials\",\"FirstCategoryId\":\"88\",\"ListUrlMain\":\"https://doi.org/10.1038/s41524-025-01538-0\",\"RegionNum\":1,\"RegionCategory\":\"材料科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"CHEMISTRY, PHYSICAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"npj Computational Materials","FirstCategoryId":"88","ListUrlMain":"https://doi.org/10.1038/s41524-025-01538-0","RegionNum":1,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CHEMISTRY, PHYSICAL","Score":null,"Total":0}
引用次数: 0

摘要

大型语言模型,通常被称为llm,在处理人工智能中一些最复杂的任务方面显示出希望。从这个角度来看,我们回顾了基础模型的更广泛领域-法学硕士是其中的一个组成部分-以及它们在材料发现领域的应用。除了目前的技术状况——包括在属性预测、合成计划和分子生成方面的应用——我们还展望了未来,并假设新的数据捕获方法,以及数据的模式,将如何影响这一新兴领域的方向。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

摘要图片

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Foundation models for materials discovery – current state and future directions

Large language models, commonly known as LLMs, are showing promise in tacking some of the most complex tasks in AI. In this perspective, we review the wider field of foundation models—of which LLMs are a component—and their application to the field of materials discovery. In addition to the current state of the art—including applications to property prediction, synthesis planning and molecular generation—we also take a look to the future, and posit how new methods of data capture, and indeed modalities of data, will influence the direction of this emerging field.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
npj Computational Materials
npj Computational Materials Mathematics-Modeling and Simulation
CiteScore
15.30
自引率
5.20%
发文量
229
审稿时长
6 weeks
期刊介绍: npj Computational Materials is a high-quality open access journal from Nature Research that publishes research papers applying computational approaches for the design of new materials and enhancing our understanding of existing ones. The journal also welcomes papers on new computational techniques and the refinement of current approaches that support these aims, as well as experimental papers that complement computational findings. Some key features of npj Computational Materials include a 2-year impact factor of 12.241 (2021), article downloads of 1,138,590 (2021), and a fast turnaround time of 11 days from submission to the first editorial decision. The journal is indexed in various databases and services, including Chemical Abstracts Service (ACS), Astrophysics Data System (ADS), Current Contents/Physical, Chemical and Earth Sciences, Journal Citation Reports/Science Edition, SCOPUS, EI Compendex, INSPEC, Google Scholar, SCImago, DOAJ, CNKI, and Science Citation Index Expanded (SCIE), among others.
期刊最新文献
Anatomy of a complex crystallization pathway A foundational deep learning force field for simulating IR and Raman spectra via molecular dynamics First-principles phonon physics using the Pheasy code Refining machine learning potentials through thermodynamic theory of phase transitions High-precision texture analysis of Fe-Co metal thin films via deep-learning four-dimensional scanning transmission electron microscopy
×
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
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术
文献互助 智能选刊 最新文献 互助须知 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 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1