VASP Agent: An Agentic Framework for Autonomous First-principles Calculations

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.