MDCrow: Automating Molecular Dynamics Workflows with Large Language Models
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866913689757548544 |
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| author | Campbell, Quintina Cox, Sam Medina, Jorge Watterson, Brittany White, Andrew D. |
| author_facet | Campbell, Quintina Cox, Sam Medina, Jorge Watterson, Brittany White, Andrew D. |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2502_09565 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | MDCrow: Automating Molecular Dynamics Workflows with Large Language Models Campbell, Quintina Cox, Sam Medina, Jorge Watterson, Brittany White, Andrew D. Artificial Intelligence Chemical Physics 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. |
| title | MDCrow: Automating Molecular Dynamics Workflows with Large Language Models |
| topic | Artificial Intelligence Chemical Physics |
| url | https://arxiv.org/abs/2502.09565 |