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Modular large language model agents for multi-task computational materials science
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DOI：10.1038/s43246-025-00994-x.png)
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Abstract
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The integration of large language models (LLMs) with domain-specific computational tools provides a pathway to streamline and enhance materials science workflows. This paper introduces MatSciAgent, a multi-agent framework supporting tasks such as materials data retrieval, continuum simulation, crystal structure generation, and molecular dynamics simulation. At its core is a master agent that interprets user queries, identifies the task type, and delegates to task-specific agent(s) equipped with tools. Leveraging databases such as Materials Project and MatWeb, the framework retrieves and summarizes materials data with grounded, factual responses, addressing limitations of vanilla LLMs. When a target material is absent from databases, a generative agent can propose plausible crystal structures. For simulations, specialized agents extract parameters to perform continuum and molecular dynamics simulations using existing software or custom code. MatSciAgent demonstrates stability, with parameter extraction achieving 100% success across five runs and materials extraction consistent in 9 of 10 runs. Its modular design ensures seamless extensibility to evolve as new capabilities are integrated. Integrating large language models with domain-specific tools can streamline computational materials science workflows by enhancing efficiency and accuracy. Here, the authors present MatSciAgent, a multi-agent framework that automates tasks like data retrieval and simulations, demonstrating high stability and adaptability, thus promising significant advancements in materials research and development.
Keywords:
Computational science
Theory and computation
Materials Science
general
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