用多智能体自动完成孔材料模拟设置与力场提取，加速新材料发现。
Towards Fully Automated Molecular Simulations: Multi-Agent Framework for Simulation Setup and Force Field Extraction
- 基于大模型的多智能体协作规划模拟流程并组装力场。
- 在文献指导下实现力场提取与RASPA模拟自动配置，正确率高。
- 适合需要快速验证材料性能的研究者，尤其适合自动化科研场景。
自动化表征多孔材料有望加速材料发现，但受限于模拟设置和力场选择的复杂性。本文提出一种多智能体框架，其中基于大语言模型的智能体可自主理解表征任务，规划合适的模拟，组装相关力场，执行模拟并解读结果以指导后续步骤。作为迈向这一愿景的第一步，我们构建了一个结合文献信息的力场提取与RASPA模拟自动设置的多智能体系统。初步评估显示其具有高正确性和可复现性，凸显该方法在实现全自动化、可扩展材料表征方面的潜力。
原文摘要 · Abstract (English)
Automated characterization of porous materials has the potential to accelerate materials discovery, but it remains limited by the complexity of simulation setup and force field selection. We propose a multi-agent framework in which LLM-based agents can autonomously understand a characterization task, plan appropriate simulations, assemble relevant force fields, execute them and interpret their results to guide subsequent steps. As a first step toward this vision, we present a multi-agent system for literature-informed force field extraction and automated RASPA simulation setup. Initial evaluations demonstrate high correctness and reproducibility, highlighting this approach's potential to enable fully autonomous, scalable materials characterization.
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