AutoMS: Multi-Agent Evolutionary Search for Cross-Physics Inverse Microstructure Design

Abstract

Designing microstructures that satisfy coupled cross-physics objectives is a fundamental challenge in material science. This inverse design problem involves a vast, discontinuous search space where traditional topology optimization is computationally prohibitive, and deep generative models often suffer from "physical hallucinations," lacking the capability to ensure rigorous validity. To address this limitation, we introduce AutoMS, a multi-agent neuro-symbolic framework that reformulates inverse design as an LLM-driven evolutionary search. Unlike methods that treat LLMs merely as interfaces, AutoMS integrates them as "semantic navigators" to initialize search spaces and break local optima, while our novel Simulation-Aware Evolutionary Search (SAES) addresses the "blindness" of traditional evolutionary strategies. Specifically, SAES utilizes simulation feedback to perform local gradient approximation and directed parameter updates, effectively guiding the search toward physically valid Pareto frontiers. Orchestrating specialized agents (Manager, Parser, Generator, and Simulator), AutoMS achieves a state-of-the-art 83.8\% success rate on 17 diverse cross-physics tasks, nearly doubling the performance of traditional NSGA-II (43.7\%) and significantly outperforming ReAct-based LLM baselines (53.3\%). Furthermore, our hierarchical architecture reduces total execution time by 23.3\%. AutoMS demonstrates that autonomous agent systems can effectively navigate complex physical landscapes, bridging the gap between semantic design intent and rigorous physical validity.

Figures (5)

• Figure 1: The AutoMS workflow: From abstract intent to concrete microstructure design. The system takes ambiguous semantic specifications as input and operationalizes them into quantifiable targets. Through a simulation-aware evolutionary loop, AutoMS iteratively refines the design candidates, resulting in optimized, topologically coherent microstructures that satisfy the initial cross-physics demands.
• Figure 2: The AutoMS Multi-Agent Architecture for Cross-Physics Inverse Design. The framework comprises two hierarchical layers: Orchestration Layer: Governed by a Manager Agent, this layer coordinates the Parser (leveraging GraphRAG) to operationalize semantic intent into parametric targets, while the Generator, Simulator, and Reporter agents handle geometry synthesis, physical validation, and results synthesis, respectively. Optimization Layer: Functioning as the numerical engine, it executes Simulation-Aware Evolutionary Search (SAES) through four mathematical phases: Local Gradient Approximation (Perception) via Weighted Least Squares, Parameter Update (Action) using gradient-guided navigation, and Pareto-Driven Selection combined with Adaptive Weight Updates (Integration) to resolve conflicting objectives and break local optima.
• Figure 3: Visual Validation of Quantitative Benchmarks. A structural and functional comparison of the designs quantified in \ref{['tab:combined_results']}.
• Figure 4: Visualization of Optimization Trajectories. The scatter plots illustrate the evolutionary search process in the cross-physics property space ($E$ vs. $\kappa$). Candidates are color-coded by generation index to visualize temporal progression. The blue arrows trace the optimization path, highlighting the contrast between the directed convergence of AutoMS (Left) and the erratic, divergent search pattern of the baseline without SAES (Right).
• Figure 5: Gallery of Optimized Microstructures. A visualization of the best-performing unit cell for each of the 17 benchmark tasks. The diversity of the generated topologies highlights AutoMS's ability to discover physics-aware geometries