El Agente Potente：高通量智能体原子模拟
El Agente Potente: High-Throughput Agentic Atomistic Simulations
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中文总结 AI 辅助
本文提出El Agente Potente智能体系统，结合类型化执行图与编码模式，实现MLIPs驱动的原子模拟，兼顾严谨性与灵活性，并在材料发现、吸附及催化等工作流中验证了其有效性与可重复性。
中文摘要 AI 辅助
基础机器学习原子间势（MLIPs）正在通过在大化学空间中实现接近从头算的精度，同时仅需一小部分计算成本，从而变革原子模拟领域。将这些工具用于高通量性质计算的一个核心挑战是，在不损害工作流严谨性的前提下，将高层科学意图转化为自适应的模拟活动。我们引入了El Agente Potente，一个智能体系统，它结合了类型化执行图与一种互补的编码模式，用于MLIPs驱动的原子模拟。类型化执行图为标准化工作流提供结构化且具有来源感知的执行，其中大型语言模型（LLMs）被限制在规划和路由层面，而确定性的Python组件则负责科学计算和验证。作为对这种结构化执行的补充，一个编码智能体为需要更大程序灵活性的任务构建定制工作流，同时调用现有的Potente函数来支持已支持的计算。我们在计算材料发现、分子能量景观探索、吸附和催化反应工作流中展示了El Agente Potente，并进行了可重复性和LLM令牌成本的系统基准测试。这些结果确立了类型化执行图和基于代码的工作流构建作为智能体科学计算的互补机制，将受控、可审计的执行与定制原子模拟所需的灵活性相结合。
英文摘要
Foundational machine-learning interatomic potentials (MLIPs) are transforming atomistic simulations by achieving near-ab initio accuracy across large chemical spaces at a fraction of the computational cost. A central challenge in using these tools for high-throughput property calculations is translating high-level scientific intent into adaptive simulation campaigns without compromising workflow rigour. We introduce El Agente Potente, an agentic system that combines typed execution graphs with a complementary coding mode for MLIPs-driven atomistic simulations. Typed execution graphs provide structured and provenance-aware execution for standardized workflows, with large language models (LLMs) restricted to planning and routing while deterministic Python components perform scientific computation and validation. Complementing this structured execution, a coding agent constructs customized workflows for tasks requiring greater procedural flexibility while invoking existing Potente functions for supported calculations. We demonstrate El Agente Potente across computational materials discovery, molecular energy-landscape exploration, adsorption, and catalytic reaction workflows, together with systematic benchmarks of reproducibility and LLM token cost. These results establish typed execution graphs and code-based workflow construction as complementary mechanisms for agentic scientific computing, combining controlled, auditable execution with the flexibility required for customized atomistic simulations
发表机构
- University of Toronto（多伦多大学）
- Vector Institute for Artificial Intelligence（向量人工智能研究所）
- Acceleration Consortium（加速联盟）
- Canadian Institute for Advanced Research (CIFAR)（加拿大高等研究院（CIFAR））
- NVIDIA（英伟达）
机构由 AI 辅助整理，请以论文原文为准。
