El Agente: An Autonomous Agent for Quantum Chemistry

Computational chemistry tools are widely used to study the behaviour of chemical phenomena. Yet, the complexity of these tools can make them inaccessible to non-specialists and challenging even for experts. In this work, we introduce El Agente Q, an LLM-based multi-agent system that dynamically generates and executes quantum chemistry workflows from natural language user prompts. The system is built on a novel cognitive architecture featuring a hierarchical memory framework that enables flexible task decomposition, adaptive tool selection, post-analysis, and autonomous file handling and submission. El Agente Q is benchmarked on six university-level course exercises and two case studies, demonstrating robust problem-solving performance (averaging >87% task success) and adaptive error handling through in situ debugging. It also supports longer-term, multi-step task execution for more complex workflows, while maintaining transparency through detailed action trace logs. Together, these capabilities lay the foundation for increasingly autonomous and accessible quantum chemistry.

@article{zou2025_2505.02484,
  title={ El Agente: An Autonomous Agent for Quantum Chemistry },
  author={ Yunheng Zou and Austin H. Cheng and Abdulrahman Aldossary and Jiaru Bai and Shi Xuan Leong and Jorge Arturo Campos-Gonzalez-Angulo and Changhyeok Choi and Cher Tian Ser and Gary Tom and Andrew Wang and Zijian Zhang and Ilya Yakavets and Han Hao and Chris Crebolder and Varinia Bernales and Alán Aspuru-Guzik },
  journal={arXiv preprint arXiv:2505.02484},
  year={ 2025 }
}