[2603.20986] AutoMOOSE: An Agentic AI for Autonomous Phase-Field Simulation

Computer Science > Artificial Intelligence arXiv:2603.20986 (cs) [Submitted on 22 Mar 2026] Title:AutoMOOSE: An Agentic AI for Autonomous Phase-Field Simulation Authors:Sukriti Manna, Henry Chan, Subramanian K.R.S. Sankaranarayanan View a PDF of the paper titled AutoMOOSE: An Agentic AI for Autonomous Phase-Field Simulation, by Sukriti Manna and 2 other authors View PDF HTML (experimental) Abstract:Multiphysics simulation frameworks such as MOOSE provide rigorous engines for phase-field materials modeling, yet adoption is constrained by the expertise required to construct valid input files, coordinate parameter sweeps, diagnose failures, and extract quantitative results. We introduce AutoMOOSE, an open-source agentic framework that orchestrates the full simulation lifecycle from a single natural-language prompt. AutoMOOSE deploys a five-agent pipeline in which the Input Writer coordinates six sub-agents and the Reviewer autonomously corrects runtime failures without user intervention. A modular plugin architecture enables new phase-field formulations without modifying the core framework, and a Model Context Protocol (MCP) server exposes the workflow as ten structured tools for interoperability with any MCP-compatible client. Validated on a four-temperature copper grain growth benchmark, AutoMOOSE generates MOOSE input files with 6 of 12 structural blocks matching a human expert reference exactly and 4 functionally equivalent, executes all runs in parallel with a 1.8x speed...