Materials science in the era of large language models: a perspective

Материаловедение в эпоху больших языковых моделей: перспективный обзор
Samuel J. Cooper, Ge Lei, R. Docherty
2024-01-01
SCID:  54.1/pybzanr6 Discuss with AI
knowledge extractionlarge language modelsmaterials scienceprocess automation
Abstract (AI)
This perspective paper explores the potential of Large Language Models (LLMs) in materials science, highlighting their abilities to handle ambiguous tasks, automate processes, and extract knowledge at scale across various disciplines.
Key Findings
1
LLMs can extract and organize materials knowledge at scale from large bodies of information.
2
LLMs can handle ambiguous materials-science tasks that are challenging to formalize with conventional methods.
3
LLMs may automate materials-science processes, improving workflow efficiency and reducing manual effort.
4
Large language models have potential to support materials science across multiple disciplines.
Research Object

the application of Large Language Models (LLMs) in materials science

Research Subject

LLM capabilities for handling ambiguous tasks, automating processes, and extracting knowledge at scale

Publication Details
Publication Date
2024-01-01
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Cited by
105
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Author Information
Authors
Samuel J. Cooper
Ge Lei
R. Docherty
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