# AutoMOOSE: An Agentic AI for Autonomous Phase-Field Simulation

arXiv: https://arxiv.org/abs/2603.20986 · HTML: https://mlsanity.com/paper/2603.20986/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Placed in Medical and scientific domain agents, one of 16 directions in LLM agents and tool use.
- Builds on 11 of its 35 references that are in the map.
- First posted on arXiv 22 Mar 2026.

## Placed in

- LLM agents and tool use / [Medical and scientific domain agents](https://mlsanity.com/agents/medical-and-scientific-domain-agents/)

## Builds on (most cited first, 11 of 11)

- [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022)
- [Reflexion: Language Agents with Verbal Reinforcement Learning](https://mlsanity.com/paper/2303.11366/) (2023)
- [Toolformer: Language Models Can Teach Themselves to Use Tools](https://mlsanity.com/paper/2302.04761/) (2023)
- [MechGPT, a language-based strategy for mechanics and materials modeling that connects knowledge across scales, disciplines and modalities](https://mlsanity.com/paper/2310.10445/) (2023)
- [AtomAgents: Alloy design and discovery through physics-aware multi-modal multi-agent artificial intelligence](https://mlsanity.com/paper/2407.10022/) (2024)
- [DREAMS: Density Functional Theory Based Research Engine for Agentic Materials Simulation](https://mlsanity.com/paper/2507.14267/) (2025)
- [Autonomous Materials Discovery Driven by Gaussian Process Regression with Inhomogeneous Measurement Noise and Anisotropic Kernels](https://mlsanity.com/paper/2006.02489/) (2020)
- [ALL-FEM: Agentic Large Language models Fine-tuned for Finite Element Methods](https://mlsanity.com/paper/2603.21011/) (2026)
- [FeaGPT: an End-to-End agentic-AI for Finite Element Analysis](https://mlsanity.com/paper/2510.21993/) (2025)
- [Good Enough Practices in Scientific Computing](https://mlsanity.com/paper/1609.00037/) (2016)
- [QUASAR: A Universal Autonomous System for Atomistic Simulation and a Benchmark of Its Capabilities](https://mlsanity.com/paper/2602.00185/) (2026)

## Built on by (most cited first, 2 of 2)

- [Socratic agents for autonomous scientific discovery in high-dimensional physical systems](https://mlsanity.com/paper/2606.26722/) (2026)
- [AutoMOOSE: Use Case and Logical Views of Agentic Phase-Field Simulation Software](https://mlsanity.com/paper/2608.20571/) (2026)

## Abstract (arXiv)

Phase-field modeling links thermodynamics and kinetics to microstructural evolution, but multiphysics frameworks such as MOOSE require expertise to construct inputs, manage campaigns, diagnose failures, and validate results. We introduce AutoMOOSE, an open-source multi-agent framework that orchestrates the simulation lifecycle from a single natural-language prompt. Six specialized agents--Architect, Input Writer, Runner, Reviewer, Visualization, and a physics-grounded Skeptic--generate, execute, analyze, and adversarially test simulations against conservation laws, asymptotic limits, and scaling relations. We validate AutoMOOSE in two domains: non-conserved copper grain growth governed by Allen-Cahn dynamics and conserved Fe-Cr spinodal decomposition governed by Cahn-Hilliard dynamics. On a prospectively specified 25-task grain-growth benchmark spanning temperature, grain count, resolution, and model formulation, AutoMOOSE generates valid inputs for all 25 tasks, completes 19 simulations with measurable coarsening, and delivers 15 results that satisfy Burke-Turnbull kinetics (R2 ≥ 0.90) and survive Skeptic falsification. The failures trace to two identifiable generator defects. An ensemble of 1000 simulations recovers the prescribed activation energy to within 1% after finite-size extrapolation (Q_∞ = 0.228 eV versus 0.230 eV). In the conserved domain, an agent-generated CALPHAD-based Fe-Cr simulation limits relative mass drift to 6.3 × 10-6, dissipates free energy to a stable plateau, and evolves toward the equilibrium tie-line. Controlled ablations show that the full pipeline converts stochastic raw generation--3-7 successful tasks out of 8 across repeated trials--into consistent success on all 8 tasks. AutoMOOSE therefore provides a practical route from natural-language specification to reproducible, physics-validated phase-field simulation campaigns.