El Agente: An autonomous agent for quantum chemistry

YH Zou and AH Cheng and A Aldossary and JR Bai and SX Leong and JA Campos-Gonzalez-Angulo and C Choi and CT Ser and G Tom and AD Wang and ZJ Zhang and I Yakavets and H Hao and C Crebolder and V Bernales and A Aspuru-Guzik, MATTER, 8, 102263 (2025).

DOI: 10.1016/j.matt.2025.102263

Computational chemistry tools are widely used to study the behavior 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.

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