The expansion of programmatically-accessible materials data has cultivated opportunities for data-driven approaches. Highly-automated frameworks like AFLOW not only manage the generation, storage, and dissemination of materials data, but also leverage the information for thermodynamic formability modeling, such as the prediction of phase diagrams and properties of disordered materials. In combination with standardized parameter sets, the wealth of data is ideal for training machine learning algorithms, which have already been employed for property prediction, descriptor development, design rule discovery, and the identification of candidate functional materials. These methods promise to revolutionize the path to synthesis and, ultimately, transform the practice of traditional materials discovery to one of rational and autonomous materials design.
Autonomous Inorganic Materials Discovery via Multi-Agent Physics-Aware Scientific Reasoning
Laboratory for Atomistic and Molecular Mechanics (LAMM) Massachusetts Institute of Technology（原子与分子力学实验室（LAMM）麻省理工学院）；Center for Computational Science and Engineering Schwarzman College of Computing Massachusetts Institute of Technology（计算科学与工程中心施瓦茨曼计算学院麻省理工学院）
Physics in the Machine: Integrating Physical Knowledge in Autonomous Phase-Mapping
National Institute of Standards and Technology（美国国家标准与技术研究院）；University of Maryland（马里兰大学）；SLAC National Accelerator Laboratory（SLAC国家加速器实验室）；Duke University（杜克大学）
所有生物有机体，从植物到生物，都可以治愈轻微的伤口和损伤。几十年来，在无机材料中实现类似的自愈能力一直是设计目标。这将代表材料工程的突破，使许多新的技术应用成为可能，因为这种材料将能够抵抗电磁辐射和/或机械冲击造成的损害。在这里，我们证明了高熵氧化物本质上能够进行自主的自我修复过程。透射电子显微镜显示，尖晶石结构的（AlCoCrCu0.5FeNi）3 O 4可以再生，并在原子水平上修复损坏时。密度泛函理论计算揭示，在制造过程中存储在高熵材料中的额外焓可以被释放，以通过重新生长成部分有序状态来有效地修复宏观缺陷。这种非凡的自修复现象使这种新材料非常适合作为涂层，使在恶劣环境中使用的结构能够更好地承受损坏，例如太空中的宇宙辐射，核电设施中的核辐射或摩擦学损坏。最重要的是，我们的研究结果为自修复材料的合成提供了一般的设计原则。
Autonomous discovery of new structure-plausibility laws for explainable and rapid crystal diagnosis and screening
Hong Kong University of Science and Technology（香港科技大学）；IAS Center for AI for Scientific Discoveries（IAS科学发现人工智能中心）；Department of Chemical and Biological Engineering（化学工程与生物工程系）
Agentic LLM Reasoning in a Self-Driving Laboratory for Air-Sensitive Lithium Halide Spinel Conductors
University of California, Berkeley（加州大学伯克利分校）；Lawrence Berkeley National Laboratory（劳伦斯伯克利国家实验室）；Korea Advanced Institute of Science and Technology（韩国科学技术院）；Florida State University（佛罗里达州立大学）；Argonne National Laboratory（阿贡国家实验室）
