用智能体自动完成第一性原理计算，确保结果可靠且可纠错。
VASP Agent: An Agentic Framework for Autonomous First-principles Calculations
- 以代码代理为核心，结合科学约束与状态监控实现多步计算
- 在4类任务中全部成功，关键参数比其他LLM流程更合理
- 支持错误诊断与恢复，适合需要高可靠性的材料计算研究
大语言模型正被嵌入科学发现的智能体框架中。第一性原理材料计算对自主性要求极高：需输入一致、长期计算监管和输出验证。本文提出 VASP Agent，一种以代码代理为中心的系统，融合可复用领域技能、确定性工具、工作区状态检查、运行时证据和科学约束，实现多步 VASP 计算。系统在结构弛豫、带隙计算、平衡晶格常数确定以及 CO/Pt(111) 吸附等多任务中均成功完成。计算结果与 pymatgen 及其他智能体工具对比显示，当出现较大偏差时，VASP Agent 生成的参数更合理。故障分析表明，传统固定流程中断的问题可在智能体控制下诊断并恢复。
原文摘要 · Abstract (English)
Large Language Models (LLMs) are increasingly embedded in agentic frameworks for scientific discovery. First-principles materials computation imposes a demanding standard for autonomy: successful execution depends on internally consistent inputs, supervision of long-running calculations, and verified outputs. Here we present VASP Agent, a coding-agent-centered system that combines reusable domain skills, deterministic tools, workspace-state inspection, runtime evidence, and scientific guardrails to execute multi-step VASP calculations. The system is evaluated across multiple tasks including structural relaxation, bandgap calculation, equilibrium lattice constant determination, and CO/Pt(111) adsorption. VASP Agent completes all evaluated cases, and its computed numerical results are compared with those obtained using pymatgen and other agentic tools. When large deviations occur, the calculation parameters produced by VASP Agent are more appropriate than those produced by LLM-based workflows. Failure analysis shows that errors that terminate fixed pipelines can be diagnosed and recovered under agentic control.
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