AutoMOOSE: An Agentic AI for Autonomous Phase-Field Simulation

• URL: http://arxiv.org/abs/2603.20986v1
• Date: Sun, 22 Mar 2026 00:11:19 GMT
• Title: AutoMOOSE: An Agentic AI for Autonomous Phase-Field Simulation
• Abstract summary: AutoMOOSE is an agentic framework that orchestrates the full simulation lifecycle from a single natural-language prompt.<n>It generates MOOSE input files with 6 of 12 structural blocks matching a human expert reference exactly and 4 functionally equivalent.<n>It performs an end-to-end physical consistency check spanning intent, finite-element execution, and Arrhenius kinetics with no human verification.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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 speedup, and performs an end-to-end physical consistency check spanning intent, finite-element execution, and Arrhenius kinetics with no human verification. Grain coarsening kinetics are recovered with R^2 = 0.90-0.95 at T >= 600 K; the recovered activation energy Q_fit = 0.296 eV is consistent with a human-written reference (Q_fit = 0.267 eV) under identical parameters. Three runtime failure classes were diagnosed and resolved autonomously within a single correction cycle, and every run produces a provenance record satisfying FAIR data principles. These results show that the gap between knowing the physics and executing a validated simulation campaign can be bridged by a lightweight multi-agent orchestration layer, providing a pathway toward AI-driven materials discovery and self-driving laboratories.

Related papers

• MLIPilot: LLM-Driven Auto-Research for Machine-Learned Interatomic Potentials [0.0]
  We introduce MLIPilot, an auto-research framework in which tool-calling large language models propose hypotheses, edit MLIP training code, launch HPC jobs, and accept or revert changes using a fixed, physically constrained scorecard.<n>We evaluate MLIPilot on MACE potential optimization using both commercial and open-weight LLM agents, including GPT-5.5, GPT-4.1, Mistral-24B, and Qwen3-32B.
  arXiv  Detail & Related papers  (2026-05-29T06:25:47Z)
• ChipMATE: Multi-Agent Training via Reinforcement Learning for Enhanced RTL Generation [55.947962672433675]
  ChipMATE is the first self-trained multi-agent framework for RTL generation.<n>Inspired by industrial practice, ChipMATE pairs a Verilog agent with a Python reference-model agent that mutually verify each other's outputs.<n>ChipMATE achieves 75.0% and 80.1% pass@1 on VerilogEval V2 with 4B and 9B base models.
  arXiv  Detail & Related papers  (2026-05-13T01:04:21Z)
• OneDrive: Unified Multi-Paradigm Driving with Vision-Language-Action Models [69.2503510410147]
  We present a unified autonomous driving framework built upon a pretrained VLM.<n>We demonstrate that pretrained VLM attention exhibits strong transferability beyond pure language modeling.<n>Experiments on end-to-end autonomous driving benchmarks demonstrate state-of-the-art performance.
  arXiv  Detail & Related papers  (2026-04-20T07:50:00Z)
• RC-NF: Robot-Conditioned Normalizing Flow for Real-Time Anomaly Detection in Robotic Manipulation [68.7948300643741]
  We propose Robot-Conditioned Normalizing Flow (RC-NF), a real-time monitoring model for robotic anomaly detection and intervention.<n>RC-NF decouples the processing of task-aware robot and object states within the normalizing flow.<n>It achieves state-of-the-art performance across all anomaly types compared to previous methods in monitoring robotic tasks.
  arXiv  Detail & Related papers  (2026-03-11T10:14:37Z)
• ComAgent: Multi-LLM based Agentic AI Empowered Intelligent Wireless Networks [62.031889234230725]
  6G networks rely on complex cross-layer optimization.<n> manually translating high-level intents into mathematical formulations remains a bottleneck.<n>We present ComAgent, a multi-LLM agentic AI framework.
  arXiv  Detail & Related papers  (2026-01-27T13:43:59Z)
• InternVLA-A1: Unifying Understanding, Generation and Action for Robotic Manipulation [77.07565723756119]
  We present InternVLA-A1, a vision-language-action model with dynamic predictive capabilities.<n>We pre-train these models on heterogeneous data sources over real-world robot data, synthetic simulation data, and human videos.<n>We evaluate InternVLA-A1 on 12 real-world robotic tasks and a simulation benchmark.
  arXiv  Detail & Related papers  (2026-01-05T18:54:29Z)
• Let It Flow: Agentic Crafting on Rock and Roll, Building the ROME Model within an Open Agentic Learning Ecosystem [90.17610617854247]
  We introduce the Agentic Learning Ecosystem (ALE), a foundational infrastructure that optimize the production pipeline for agentic model.<n>ALE consists of three components: ROLL, a post-training framework for weight optimization; ROCK, a sandbox environment manager for trajectory generation; and iFlow CLI, an agent framework for efficient context engineering.<n>We release ROME, an open-source agent grounded by ALE and trained on over one million trajectories.
  arXiv  Detail & Related papers  (2025-12-31T14:03:39Z)
• Towards Efficient Agents: A Co-Design of Inference Architecture and System [66.59916327634639]
  This paper presents AgentInfer, a unified framework for end-to-end agent acceleration.<n>We decompose the problem into four synergistic components: AgentCollab, AgentSched, AgentSAM, and AgentCompress.<n>Experiments on the BrowseComp-zh and DeepDiver benchmarks demonstrate that through the synergistic collaboration of these methods, AgentInfer reduces ineffective token consumption by over 50%.
  arXiv  Detail & Related papers  (2025-12-20T12:06:13Z)
• DynaMate: An Autonomous Agent for Protein-Ligand Molecular Dynamics Simulations [13.253932177045842]
  Force field-based molecular dynamics (MD) simulations are indispensable for probing the structure, dynamics, and functions of biomolecular systems.<n>The technical complexity of MD setup, encompassing parameterization, input preparation, and software configuration, remains a major barrier for widespread and efficient usage.<n>Here, we present DynaMate, a modular multi-agent framework that autonomously designs and executes complete MD for both protein and protein-ligand systems.
  arXiv  Detail & Related papers  (2025-12-10T19:40:51Z)
• NNGPT: Rethinking AutoML with Large Language Models [36.90850535125572]
  NNGPT is an open-source framework that turns a large language model (LLM) into a self-improving AutoML engine for neural network development.<n>It integrates within one unified workflow five synergistic LLM-based pipelines: zero-shot architecture synthesis, hyper parameter optimization, code-aware accuracy/early-stop prediction, and reinforcement learning.<n>The system has already generated over 5K validated models, proving NNGPT as an autonomous AutoML engine.
  arXiv  Detail & Related papers  (2025-11-25T14:10:44Z)
• Rethinking Testing for LLM Applications: Characteristics, Challenges, and a Lightweight Interaction Protocol [83.83217247686402]
  Large Language Models (LLMs) have evolved from simple text generators into complex software systems that integrate retrieval augmentation, tool invocation, and multi-turn interactions.<n>Their inherent non-determinism, dynamism, and context dependence pose fundamental challenges for quality assurance.<n>This paper decomposes LLM applications into a three-layer architecture: textbftextitSystem Shell Layer, textbftextitPrompt Orchestration Layer, and textbftextitLLM Inference Core.
  arXiv  Detail & Related papers  (2025-08-28T13:00:28Z)
• Autonomous Control Leveraging LLMs: An Agentic Framework for Next-Generation Industrial Automation [0.0]
  We introduce a unified agentic framework that leverages large language models (LLMs) for both discrete fault-recovery planning and continuous process control.<n>Our results demonstrate that, with structured feedback and modular agents, LLMs can unify high-level symbolic planningand low-level continuous control.
  arXiv  Detail & Related papers  (2025-07-03T11:20:22Z)
• ChatCFD: An LLM-Driven Agent for End-to-End CFD Automation with Domain-Specific Structured Reasoning [4.098524616768554]
  ChatCFD is an automated agent system for OpenFOAM simulations.<n>Its four-stage pipeline enables iterative trial-reflection-refinement for intricate setups.<n>ChatCFD shows strong potential as a modular component in MCP-based agent networks for collaborative multi-agent systems.
  arXiv  Detail & Related papers  (2025-05-28T08:43:49Z)
• On Simulation-Guided LLM-based Code Generation for Safe Autonomous Driving Software [0.577182115743694]
  Automated Driving System (ADS) is a safety-critical software system responsible for the interpretation of the vehicle's environment.<n>Development of ADS requires rigorous processes to verify, validate, assess, and qualify the code before it can be deployed in the vehicle.<n>This study developed and evaluated a prototype for automatic code generation and assessment.
  arXiv  Detail & Related papers  (2025-04-02T21:35:11Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.