VASP Agent: An Agentic Framework for Autonomous First-principles Calculations
Organizations: Shanghai Artificial Intelligence Laboratory, Shanghai, 200232, China. · Department of Computer Science and Technology, Tsinghua University, Beijing, 100084, China. · School of Physical Science and Technology, ShanghaiTech University, Shanghai, 201210, China. · Department of Automation, Tsinghua University, Beijing, 100084, China. · School of Chemistry and Chemical Engineering, The Queen’s University of Belfast, Belfast, BT9 5AG, UK. · Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing, 100084, China. · Key Laboratory of Computational Physical Sciences (Ministry of Education), Institute of Computational Physical Sciences, State Key Laboratory of Surface Physics, Department of Physics, Fudan University, Shanghai, 200433, China. · The Chinese University of Hong Kong, Hong Kong, 999077, China. · Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China.
Abstract
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.