用智能框架自动设计高性能合金，效率提升数倍。
Autonomous Multi-objective Alloy Design through Simulation-guided Optimization
- 融合大模型与模拟计算，闭环优化合金成分。
- 钛合金密度降8.1%、强度升13%，性能超越航天基准。
- 适合材料研发人员快速探索高熵合金新配方。
合金发现受限于庞大的成分空间、相互竞争的设计目标以及高昂的实验成本。尽管模拟和机器学习各自加速了部分流程，但将科学知识、可扩展搜索与实验验证统一到数据高效的流程中仍具挑战。本文提出AutoMAT，一个从概念生成到实验验证的分层自主框架。该框架整合大语言模型、自动化CALPHAD模拟、基于残差学习的修正方法及AI引导的优化策略，可将设计目标转化为候选合金，通过闭环计算搜索优化成分，并在无需人工标注数据集的情况下实现实验验证。针对轻质高强合金设计，AutoMAT发现的钛合金密度比航空航天基准材料Ti-185低8.1%，强度高出13.0%，具体强度为所有对比系统中最高。在另一案例中，该框架发现的高熵合金屈服强度比基线提升28.2%，同时保持良好延展性。AutoMAT将合金发现周期从数年压缩至数周，为自主材料设计提供通用路径。
原文摘要 · Abstract (English)
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.
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