Alloy discovery is constrained by vast compositional spaces, competing objectives, and prohibitive experimental costs. Although simulations and machine learning have each accelerated parts of this process, unifying scientific knowledge, scalable search, and experimental confirmation into a data-efficient workflow remains challenging. Here, we present AutoMAT, a hierarchical autonomous framework spanning ideation to experimental validation. Integrating large language models, automated CALPHAD simulations, residual-learning-based correction, and AI-guided optimization, AutoMAT translates design targets into candidate alloys, refines compositions through closed-loop computational search, and validates results experimentally without hand-curated datasets. Targeting lightweight, high-strength alloys, AutoMAT identifies a titanium alloy 8.1% less dense and 13.0% stronger than the aerospace benchmark Ti-185, achieving the highest specific strength among benchmarked systems. In a second case, AutoMAT discovers a high-entropy alloy with 28.2% higher yield strength than the baseline while preserving high ductility. AutoMAT compresses alloy discovery from years to weeks, establishing a generalizable route toward autonomous materials design.
@article{arxiv.2507.16005,
title = {Autonomous Multi-objective Alloy Design through Simulation-guided Optimization},
author = {Penghui Yang and Chendong Zhao and Bijun Tang and Zhonghan Zhang and Xinrun Wang and Yanchen Deng and Xuyu Dong and Yuhao Lu and Jianguo Huang and Yixuan Li and Yushan Xiao and Cuntai Guan and Zheng Liu and Bo An},
journal= {arXiv preprint arXiv:2507.16005},
year = {2026}
}