标题： MDCrow：使用大型语言模型自动化分子动力学工作流程 显示英文标题

标题： MDCrow: Automating Molecular Dynamics Workflows with Large Language Models

摘要： 分子动力学（MD）模拟对于理解生物分子系统至关重要，但自动化仍然具有挑战性。 大型语言模型（LLM）的最新进展已经展示了使用基于LLM的代理自动执行复杂科学任务的成功。 在本文中，我们介绍了MDCrow，一个能够自动化MD工作流的代理LLM助手。 MDCrow通过40个专家设计的工具进行思维链，以处理和处理文件、设置模拟、分析模拟输出以及从文献和数据库中检索相关信息。 我们在25个具有不同所需子任务和难度的任务上评估了MDCrow的性能，并评估了代理对难度和提示风格的鲁棒性。 \texttt{gpt-4o}能够以低方差完成复杂任务，紧随其后的是\texttt{llama3-405b}，这是一个引人注目的开源模型。 虽然提示风格不影响最佳模型的性能，但它对较小的模型有显著影响。 显示英文摘要

摘要： Molecular dynamics (MD) simulations are essential for understanding biomolecular systems but remain challenging to automate. Recent advances in large language models (LLM) have demonstrated success in automating complex scientific tasks using LLM-based agents. In this paper, we introduce MDCrow, an agentic LLM assistant capable of automating MD workflows. MDCrow uses chain-of-thought over 40 expert-designed tools for handling and processing files, setting up simulations, analyzing the simulation outputs, and retrieving relevant information from literature and databases. We assess MDCrow's performance across 25 tasks of varying required subtasks and difficulty, and we evaluate the agent's robustness to both difficulty and prompt style. \texttt{gpt-4o} is able to complete complex tasks with low variance, followed closely by \texttt{llama3-405b}, a compelling open-source model. While prompt style does not influence the best models' performance, it has significant effects on smaller models.