Modular large language model agents for multi-task computational materials science

Akshat Chaudhari

doi:10.1038/s43246-025-00994-x

2026-03-26

Akshat Chaudhari

Janghoon Ock *

Amir Barati Farimani *

DOI：10.1038/s43246-025-00994-x

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摘要

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将大型语言模型（LLMs）与领域特定的计算工具相结合，为简化和提升材料科学工作流程提供了一条途径。本文介绍了MatSciAgent，一个支持材料数据检索、连续体模拟、晶体结构生成和分子动力学模拟等任务的多智能体框架。其核心是一个主智能体，用于解释用户查询、识别任务类型，并将任务委派给配备工具的任务特定智能体。利用Materials Project和MatWeb等数据库，该框架能够检索并总结材料数据，以基于事实的响应提供信息，解决了原始LLM的局限性。当目标材料不在数据库中时，生成型智能体可以提出合理的晶体结构。对于模拟任务，专业智能体提取参数，使用现有软件或自定义代码执行连续体和分子动力学模拟。MatSciAgent展示了其稳定性，参数提取在五次运行中均达到100%成功率，材料提取在十次运行中有九次保持一致。其模块化设计确保了无缝扩展性，以便随着新功能的集成而演进。将大型语言模型与领域特定工具相结合，可以通过提高效率和准确性来简化计算材料科学工作流程。在此，作者介绍了MatSciAgent，一个自动化数据检索和模拟任务的多智能体框架，展示了高稳定性和适应性，有望在材料研究和开发领域取得重大进展。

Keyword:

Computational science

Theory and computation

Materials Science

general

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期刊

Communications Materials

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9.6

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1.4K

被引数:

4.3K

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carnegie mellon university

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