The design of alloys is a multi-scale problem that requires a holistic approach that involves retrieving relevant knowledge, applying advanced computational methods, conducting experimental validations, and analyzing the results, a process that is typically reserved for human experts. Machine learning (ML) can help accelerate this process, for instance, through the use of deep surrogate models that connect structural features to material properties, or vice versa. However, existing data-driven models often target specific material objectives, offering limited flexibility to integrate out-of-domain knowledge and cannot adapt to new, unforeseen challenges. Here, we overcome these limitations by leveraging the distinct capabilities of multiple AI agents that collaborate autonomously within a dynamic environment to solve complex materials design tasks. The proposed physics-aware generative AI platform, AtomAgents, synergizes the intelligence of large language models (LLM) the dynamic collaboration among AI agents with expertise in various domains, including knowledge retrieval, multi-modal data integration, physics-based simulations, and comprehensive results analysis across modalities that includes numerical data and images of physical simulation results. The concerted effort of the multi-agent system allows for addressing complex materials design problems, as demonstrated by examples that include autonomously designing metallic alloys with enhanced properties compared to their pure counterparts. Our results enable accurate prediction of key characteristics across alloys and highlight the crucial role of solid solution alloying to steer the development of advanced metallic alloys. Our framework enhances the efficiency of complex multi-objective design tasks and opens new avenues in fields such as biomedical materials engineering, renewable energy, and environmental sustainability.

翻译：合金设计是一个多尺度问题，需要采用整体性方法，涉及检索相关知识、应用先进计算方法、进行实验验证以及分析结果，这一过程通常仅限于人类专家。机器学习（ML）可帮助加速此过程，例如通过使用将结构特征与材料性能相关联（或反之）的深度代理模型。然而，现有的数据驱动模型往往针对特定的材料目标，在整合领域外知识方面灵活性有限，且无法适应新的、不可预见的挑战。在此，我们通过利用多个AI智能体在动态环境中自主协作以解决复杂材料设计任务的独特能力，克服了这些限制。所提出的物理感知生成式AI平台——AtomAgents，协同整合了大型语言模型（LLM）的智能、具备多领域专业知识（包括知识检索、多模态数据集成、基于物理的仿真以及涵盖数值数据和物理仿真结果图像的多模态综合分析）的AI智能体之间的动态协作。多智能体系统的协同努力使得能够应对复杂的材料设计问题，如通过自主设计性能优于纯金属对应物的金属合金等示例所展示。我们的结果能够准确预测合金的关键特性，并凸显了固溶合金化在引导先进金属合金发展中的关键作用。该框架提升了复杂多目标设计任务的效率，并为生物医学材料工程、可再生能源及环境可持续性等领域开辟了新途径。