Can LLMs automate quantum chemistry workflows?
El Agente: An Autonomous Agent for Quantum Chemistry
May 6, 2025
https://arxiv.org/pdf/2505.02484This paper introduces El Agente Q, a multi-agent AI system designed to automate and simplify complex quantum chemistry calculations using large language models (LLMs). It translates natural language user requests into workflows, executes them using relevant software tools, and generates reports, making computational chemistry more accessible.
El Agente Q features a hierarchical network of specialized LLM agents, enabling effective delegation of tasks and efficient context management. Key features relevant to LLM-based multi-agent systems include:
- Hierarchical structure: A top-level LLM agent acts as a "computational chemist," delegating tasks to specialized sub-agents for geometry optimization, quantum calculations, and file management, minimizing cognitive load for individual LLMs.
- Dynamic workflow generation: It dynamically creates workflows based on user requests rather than relying on fixed pipelines, offering flexibility and adaptability.
- Error handling and recovery: The system incorporates adaptive error handling and debugging, autonomously correcting input/output errors and restarting tasks as needed.
- Action trace export: El Agente Q logs agent actions and can export them as Python code, aiding in understanding, verifying, and reusing workflows.
- Human-in-the-loop capabilities: It facilitates human interaction via a chat interface and allows expert knowledge integration through programming and natural language instructions.