AI Agents 相关度: 9/10

El Agente Quntur: A research collaborator agent for quantum chemistry

Juan B. Pérez-Sánchez, Yunheng Zou, Jorge A. Campos-Gonzalez-Angulo, Marcel Müller, Ignacio Gustin, Andrew Wang, Han Hao, Tsz Wai Ko, Changhyeok Choi, Eric S. Isbrandt, Mohammad Ghazi Vakili, Hanyong Xu, Chris Crebolder, Varinia Bernales, Alán Aspuru-Guzik
arXiv: 2602.04850v1 发布: 2026-02-04 更新: 2026-02-04

AI 摘要

El Agente Quntur是一个用于量子化学的智能体,旨在成为研究合作者并扩展其应用。

主要贡献

  • 设计并实现了名为Quntur的AI智能体系统
  • 提出了reasoning-driven决策、通用可组合行为和引导式深度研究的设计策略
  • 验证了Quntur在ORCA软件中的应用,并展望了全自动计算化学研究智能体的未来

方法论

采用分层多智能体系统,通过reasoning-driven决策,通用可组合行为,和guided deep research 实现自动化化学实验。

原文摘要

Quantum chemistry is a foundational enabling tool for the fields of chemistry, materials science, computational biology and others. Despite of its power, the practical application of quantum chemistry simulations remains in the hands of qualified experts due to methodological complexity, software heterogeneity, and the need for informed interpretation of results. To bridge the accessibility gap for these tools and expand their reach to chemists with broader backgrounds, we introduce El Agente Quntur, a hierarchical, multi-agent AI system designed to operate not merely as an automation tool but as a research collaborator for computational quantum chemistry. Quntur was designed following three main strategies: i) elimination of hard-coded procedural policies in favour of reasoning-driven decisions, ii) construction of general and composable actions that facilitate generalization and efficiency, and iii) implementation of guided deep research to integrate abstract quantum-chemical reasoning across subdisciplines and a detailed understanding of the software's internal logic and syntax. Although instantiated in ORCA, these design principles are applicable to research agents more generally and easily expandable to additional quantum chemistry packages and beyond. Quntur supports the full range of calculations available in ORCA 6.0 and reasons over software documentation and scientific literature to plan, execute, adapt, and analyze in silico chemistry experiments following best practices. We discuss the advances and current bottlenecks in agentic systems operating at the research level in computational chemistry, and outline a roadmap toward a fully autonomous end-to-end computational chemistry research agent.

标签

量子化学 AI智能体 自动化 计算化学

arXiv 分类

physics.chem-ph cs.AI cs.MA