Materials design often relies on human-generated hypotheses, a process inherently limited by cognitive constraints such as knowledge gaps and limited ability to integrate and extract knowledge implications, particularly when multidisciplinary expertise is required. This work demonstrates that large language models (LLMs), coupled with prompt engineering, can effectively generate non-trivial materials hypotheses by integrating scientific principles from diverse sources without explicit design guidance by human experts. These include design ideas for high-entropy alloys with superior cryogenic properties and halide solid electrolytes with enhanced ionic conductivity and formability. These design ideas have been experimentally validated in high-impact publications in 2023 not available in the LLM training data, demonstrating the LLM's ability to generate highly valuable and realizable innovative ideas not established in the literature. Our approach primarily leverages materials system charts encoding processing-structure-property relationships, enabling more effective data integration by condensing key information from numerous papers, and evaluation and categorization of numerous hypotheses for human cognition, both through the LLM. This LLM-driven approach opens the door to new avenues of artificial intelligence-driven materials discovery by accelerating design, democratizing innovation, and expanding capabilities beyond the designer's direct knowledge.
AUV-Bench: Aesthetic Understanding and Generation Evaluation for User Interfaces
The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；Alibaba Group（阿里巴巴集团）；The Hong Kong University of Science and Technology（香港科技大学）
Beijing Institute of Technology（北京理工大学）；Peking University（北京大学）；Yangtze Delta Region Academy of Beijing Institute of Technology（北京理工大学长三角研究院）；BIGAI（北京通用人工智能研究院）
What Should Embeddings Embed? Autoregressive Models Represent Latent Generating Distributions
Department of Computer Science（计算机科学系）；Princeton University（普林斯顿大学）；Stanford University（斯坦福大学）；Department of Linguistics and Wu Tsai Institute（语言学系和吴泰教授研究所）；Yale University（耶鲁大学）；Anthropic（Anthropic公司）；Departments of Psychology and Computer Science（心理学系和计算机科学系）
MOOSE-Chem2: Exploring LLM Limits in Fine-Grained Scientific Hypothesis Discovery via Hierarchical Search
Nanyang Technological University（南洋理工大学）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；University of Science and Technology of China（中国科学技术大学）；Wuhan University（武汉大学）；National University of Singapore（新加坡国立大学）；University of New South Wales（新南威尔士大学）；MiroMind；The Chinese University of Hong Kong（香港中文大学）
Automating Exploratory Proteomics Research via Language Models
Tsinghua University（清华大学）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；Frontis AI；National Center for Protein Sciences (Beijing)（国家蛋白质科学中心（北京））；Beijing Proteome Research Center（北京蛋白质组研究中心）；International Academy of Phronesis Medicine (Guangdong)（国际实践医学研究院（广东））
Generating Hypotheses of Dynamic Causal Graphs in Neuroscience: Leveraging Generative Factor Models of Observed Time Series
Department of Electrical and Computer Engineering, Duke University, Durham, NC 15213, USA（电气与计算机工程系，杜克大学，达勒姆，NC 15213，美国）；Departments of Civil and Environmental Engineering（土木与环境工程系；生物统计学与生物信息学系；和计算机科学系，杜克大学，达勒姆，NC 15213，美国）；Biostatistics and Bioinformatics；and Computer Science, Duke University, Durham, NC 15213, USA
Text2BIM: Generating Building Models Using a Large Language Model-based Multi-Agent Framework
Chair of Computing in Civil and Building Engineering, Technical University of Munich, Munich, Germany（土木与建筑工程计算系，慕尼黑技术大学，德国慕尼黑）；TUM Georg Nemetschek Institute, Munich, Germany（慕尼黑技术大学格奥尔·奈梅克研究所，德国慕尼黑）
Automating modeling in mechanics: LLMs as designers of physics-constrained neural networks for constitutive modeling of materials
Institute of Material Systems Modeling, Helmholtz-Zentrum Hereon（材料系统建模研究所，海德堡中心）；Institute for Continuum and Material Mechanics, Hamburg University of Technology（连续力学与材料力学研究所，汉堡技术大学）；Helmholtz-Zentrum Hereon（海德堡中心）；Hamburg University of Technology（汉堡技术大学）
Causal Modeling of Adverse Pregnancy Outcomes via Adaptive LLM Proposals
The University of Texas at Dallas（德克萨斯大学达拉斯分校）；Technical University of Darmstadt（达姆施塔特工业大学）；Swarthmore College（斯沃斯莫尔学院）；Indiana University School of Medicine（印第安纳大学医学院）；Hessian Center for Artificial Intelligence（黑森人工智能中心）；German Research Center for AI (DFKI)（德国人工智能研究中心（DFKI））
