AutoMS: Multi-Agent Evolutionary Search for Cross-Physics Inverse Microstructure Design
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866913023842582528 |
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| author | Zhao, Zhenyuan Xing, Yu Xue, Tianyang Cao, Lingxin Yan, Xin Lu, Lin |
| author_facet | Zhao, Zhenyuan Xing, Yu Xue, Tianyang Cao, Lingxin Yan, Xin Lu, Lin |
| contents | Designing microstructures with coupled cross-physics objectives is a fundamental challenge where traditional topology optimization is often computationally prohibitive and deep generative models frequently suffer from physical hallucinations. We introduce AutoMS, a multi-agent neuro-symbolic framework that reformulates inverse design as an LLM-driven evolutionary search. AutoMS leverages LLMs as semantic navigators to decompose complex requirements and coordinate agent workflows, while a novel Simulation-Aware Evolutionary Search (SAES) mechanism handles low-level numerical optimization via local gradient approximation and directed parameter updates. This architecture achieves a state-of-the-art 83.8% success rate on 17 diverse cross-physics tasks, significantly outperforming both traditional evolutionary algorithms and existing agentic baselines. By decoupling open-ended semantic orchestration from simulation-grounded numerical search, AutoMS provides a robust pathway for navigating complex physical landscapes that remain intractable for standard generative or purely linguistic approaches. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_27195 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | AutoMS: Multi-Agent Evolutionary Search for Cross-Physics Inverse Microstructure Design Zhao, Zhenyuan Xing, Yu Xue, Tianyang Cao, Lingxin Yan, Xin Lu, Lin Artificial Intelligence Designing microstructures with coupled cross-physics objectives is a fundamental challenge where traditional topology optimization is often computationally prohibitive and deep generative models frequently suffer from physical hallucinations. We introduce AutoMS, a multi-agent neuro-symbolic framework that reformulates inverse design as an LLM-driven evolutionary search. AutoMS leverages LLMs as semantic navigators to decompose complex requirements and coordinate agent workflows, while a novel Simulation-Aware Evolutionary Search (SAES) mechanism handles low-level numerical optimization via local gradient approximation and directed parameter updates. This architecture achieves a state-of-the-art 83.8% success rate on 17 diverse cross-physics tasks, significantly outperforming both traditional evolutionary algorithms and existing agentic baselines. By decoupling open-ended semantic orchestration from simulation-grounded numerical search, AutoMS provides a robust pathway for navigating complex physical landscapes that remain intractable for standard generative or purely linguistic approaches. |
| title | AutoMS: Multi-Agent Evolutionary Search for Cross-Physics Inverse Microstructure Design |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2603.27195 |