Opportunities for retrieval and tool augmented large language models in scientific facilities

IF 9.4 1区 材料科学 Q1 CHEMISTRY, PHYSICAL npj Computational Materials Pub Date : 2024-11-05 DOI:10.1038/s41524-024-01423-2
Michael H. Prince, Henry Chan, Aikaterini Vriza, Tao Zhou, Varuni K. Sastry, Yanqi Luo, Matthew T. Dearing, Ross J. Harder, Rama K. Vasudevan, Mathew J. Cherukara
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Abstract

Upgrades to advanced scientific user facilities such as next-generation x-ray light sources, nanoscience centers, and neutron facilities are revolutionizing our understanding of materials across the spectrum of the physical sciences, from life sciences to microelectronics. However, these facility and instrument upgrades come with a significant increase in complexity. Driven by more exacting scientific needs, instruments and experiments become more intricate each year. This increased operational complexity makes it ever more challenging for domain scientists to design experiments that effectively leverage the capabilities of and operate on these advanced instruments. Large language models (LLMs) can perform complex information retrieval, assist in knowledge-intensive tasks across applications, and provide guidance on tool usage. Using x-ray light sources, leadership computing, and nanoscience centers as representative examples, we describe preliminary experiments with a Context-Aware Language Model for Science (CALMS) to assist scientists with instrument operations and complex experimentation. With the ability to retrieve relevant information from facility documentation, CALMS can answer simple questions on scientific capabilities and other operational procedures. With the ability to interface with software tools and experimental hardware, CALMS can conversationally operate scientific instruments. By making information more accessible and acting on user needs, LLMs could expand and diversify scientific facilities’ users and accelerate scientific output.

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科学设施中的检索和工具增强大型语言模型的机遇
新一代 X 射线光源、纳米科学中心和中子设施等先进科学用户设施的升级正在彻底改变我们对从生命科学到微电子学等物理科学领域材料的认识。然而,这些设施和仪器的升级也带来了复杂性的显著增加。在更加严格的科学需求的驱动下,仪器和实验每年都变得更加复杂。操作复杂性的增加使得领域科学家在设计实验时,如何有效利用这些先进仪器的功能并在其上进行操作变得越来越具有挑战性。大型语言模型(LLM)可以执行复杂的信息检索,协助跨应用领域的知识密集型任务,并为工具的使用提供指导。我们以 X 射线光源、领导力计算和纳米科学中心为代表,介绍了使用 "情境感知科学语言模型"(CALMS)协助科学家进行仪器操作和复杂实验的初步实验。CALMS 能够从设施文档中检索相关信息,因此可以回答有关科学能力和其他操作程序的简单问题。凭借与软件工具和实验硬件接口的能力,CALMS 能够以对话方式操作科学仪器。通过使信息更容易获取并根据用户需求采取行动,本地化学习管理系统可以扩大科学设施的用户并使其多样化,加快科学产出。
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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.
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