AI助手Aitomia让原子与量子化学模拟更简单,支持从新手到专家全流程操作。
Aitomia: Your Intelligent Assistant for AI-Driven Atomistic and Quantum Chemical Simulations
- 集成大模型与多智能体,自动完成从设置到分析的模拟流程
- 兼容DFT、GFN2-xTB等主流方法,支持几何优化、热化学等10余类任务
- 适合科研人员快速上手,降低原子模拟门槛
我们开发了Aitomia——一个由人工智能驱动的智能助手平台,用于辅助开展原子级和量子化学(QC)模拟。该平台集成了聊天机器人和AI智能体,可帮助专家与非专家用户完成模拟设置、运行、结果分析及图文总结。Aitomia融合基于大语言模型的智能体与MLatom平台,支持包括密度泛函理论(DFT)、半经验方法(如GFN2-xTB)以及部分高阶波函数方法在内的常规量子化学计算,并通过Gaussian、ORCA、PySCF、xtb等常用程序接口,覆盖基态与激发态计算、几何优化、热化学、光谱模拟等任务。其多智能体架构可自主执行复杂工作流,如反应焓变计算。Aitomia是首个在云端公开发布的面向广义原子模拟的智能助手平台(厦门大学Aitomistic Lab@XMU:https://atom.xmu.edu.cn;Aitomistic Hub:https://aitomistic.xyz),显著降低原子模拟技术门槛,推动相关领域研究与研发的普及与加速。
原文摘要 · Abstract (English)
We have developed Aitomia - a platform powered by AI to assist in performing AI-driven atomistic and quantum chemical (QC) simulations. This evolving intelligent assistant platform is equipped with chatbots and AI agents to help experts and guide non-experts in setting up and running atomistic simulations, analyzing simulation results, and summarizing them for the user in both textual and graphical forms. Aitomia combines LLM-based agents with the MLatom platform to support AI-driven atomistic simulations as well as conventional quantum-chemical calculations, including DFT, semiempirical methods such as GFN2-xTB, and selected high-level wavefunction-based methods, through interfaces to widely used programs such as Gaussian, ORCA, PySCF, and xtb, covering tasks from ground-state and excited-state calculations to geometry optimization, thermochemistry, and spectra simulations. The multi-agent implementation enables autonomous execution of complex computational workflows, such as reaction enthalpy calculations. Aitomia was the first intelligent assistant publicly launched on cloud computing platforms for broad-scope atomistic simulations (Aitomistic Lab@XMU at https://atom.xmu.edu.cn and Aitomistic Hub at https://aitomistic.xyz). Aitomia lowers the barrier to performing atomistic simulations, thereby democratizing simulations and accelerating research and development in relevant fields.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品;中文卡片由大模型生成,请以原文为准。