Modular Large Language Model Agents for Multi-Task Computational Materials Science

The integration of large language models (LLMs) with domain-specific computational tools offers a promising pathway to streamline and enhance materials science workflows. This paper presents MatSciAgent, a multi-agent framework capable of performing key tasks such as materials data retrieval, continuum simulation, crystal structure generation, and molecular dynamics simulation. At the core of the framework is the master agent, which interprets the user’s natural language query, identifies the task type, and delegates it to a corresponding task-specific agent equipped with appropriate computational tools. Leveraging databases such as the Materials Project and MatWeb, the framework retrieves and summarizes materials data with grounded, factual responses—addressing a key limitation of vanilla LLMs. In cases where the target material is not found in existing databases, a generative task-specific agent can propose plausible crystal structures. For simulation tasks, dedicated agents extract relevant parameters from the user query to conduct continuum simulations (e.g., Cellular Automata and Monte Carlo Annealing) and atomistic simulations (e.g., Molecular Dynamics) using both established software and custom code. The modular design of these agents and their associated tools enables seamless extensibility, allowing the framework to evolve as new capabilities are integrated.