[2603.27195] AutoMS: Multi-Agent Evolutionary Search for Cross-Physics Inverse Microstructure Design

Computer Science > Artificial Intelligence arXiv:2603.27195 (cs) [Submitted on 28 Mar 2026] Title:AutoMS: Multi-Agent Evolutionary Search for Cross-Physics Inverse Microstructure Design Authors:Zhenyuan Zhao, Yu Xing, Tianyang Xue, Lingxin Cao, Xin Yan, Lin Lu View a PDF of the paper titled AutoMS: Multi-Agent Evolutionary Search for Cross-Physics Inverse Microstructure Design, by Zhenyuan Zhao and 5 other authors View PDF HTML (experimental) 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 speciali...