AutoMS achieves 83.8% success on 17 cross-physics microstructure design tasks by combining LLM-driven agent coordination with simulation-aware evolutionary search.
Output Specification:Return a strict JSON containing task type, specific target (only relevant proper- ties),material parameters, andrecommended base material
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.AI 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
AutoMS: Multi-Agent Evolutionary Search for Cross-Physics Inverse Microstructure Design
AutoMS achieves 83.8% success on 17 cross-physics microstructure design tasks by combining LLM-driven agent coordination with simulation-aware evolutionary search.