VASPilot: MCP-Facilitated Multi-Agent Intelligence for Autonomous VASP Simulations
Jiaxuan Liu, Tiannian Zhu, Caiyuan Ye, Zhong Fang, Hongming Weng, Quansheng Wu

TL;DR
VASPilot automates and streamlines VASP DFT simulations using a multi-agent system, reducing manual effort and enabling high-throughput computational materials research.
Contribution
It introduces an open-source, fully automated platform with a multi-agent architecture and standardized protocol for VASP workflows, enhancing efficiency and extensibility.
Findings
Successfully automated routine and advanced VASP benchmarks
Achieved reliable, manual-free completion of complex simulations
Facilitated easy extension to other DFT codes
Abstract
Density-functional-theory (DFT) simulations with the Vienna Ab initio Simulation Package (VASP) are indispensable in computational materials science but often require extensive manual setup, monitoring, and postprocessing. Here, we introduce VASPilot, an open-source platform that fully automates VASP workflows via a multi-agent architecture built on the CrewAI framework and a standardized Model Context Protocol (MCP). VASPilot's agent suite handles every stage of a VASP study-from retrieving crystal structures and generating input files to submitting Slurm jobs, parsing error messages, and dynamically adjusting parameters for seamless restarts. A lightweight Flask-based web interface provides intuitive task submission, real-time progress tracking, and drill-down access to execution logs, structure visualizations, and plots. We validate VASPilot on both routine and advanced benchmarks:…
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Taxonomy
TopicsMachine Learning in Materials Science · Electronic and Structural Properties of Oxides · Inorganic Chemistry and Materials
