发表机构
School of Computer Science, University of Sheffield, Sheffield, UK; Centre for Machine Intelligence, University of Sheffield, Sheffield, UK; School of Chemical, Materials and Biological Engineering, University of Sheffield, Sheffield, UK; Henry Royce Institute, Royce Discovery Centre, University of Sheffield, Sheffield, UK; Materials Nexus Ltd., Salisbury House, Cambridge, UK; School of Mathematical and Physical Sciences, University of Sheffield, Sheffield, UK（计算机科学学院，谢菲尔德大学; 智能机器研究中心，谢菲尔德大学; 化学、材料与生物工程学院，谢菲尔德大学; 亨利·罗伊奇研究所，谢菲尔德大学; 材料 nexus 有限公司，剑桥，英国; 数学与物理科学学院，谢菲尔德大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究探讨生成式和多模态人工智能在材料预测与设计中的应用，引入材料属性层次结构框架，指出当前证据局限，强调需全社区标准支持多模态相关建模与基准测试，以设计具科学和实际新颖性、可实验实现的材料。
AI 中文摘要
人工智能通过有效探索化学和结构空间加速材料预测和设计，对发现新型材料前景广阔。然而，材料发现中的新颖性包含化学合理性、结构独特性、性能相关性和实验可实现性，使得人工智能驱动的新颖性声明难以证实。我们引入材料属性层次结构，从内在的成分决定属性到外在的加工依赖性能，以阐明部署约束并区分结构、物理和部署新颖性。该框架激发了基于证据的多模态材料数据观，表明当前证据仍集中在成分和理想化结构，而异质、代表性不足和弱整合的模态限制了对物理和部署新颖性的支持。它还突出了主要基于计算标签和代理新颖性标准的基准测试的局限性。需要全社区的数据收集、模态对齐和证据合成标准，以支持多模态数据构建、过程感知多模态建模、可行性优先生成建模和部署感知基准测试，使生成式和多模态人工智能能够设计具有可靠科学和实际新颖性的可实验实现的材料。
英文摘要
Artificial intelligence (AI) is accelerating materials prediction and design by enabling efficient exploration of chemical and structural spaces, with particular promise for novel materials discovery. However, novelty in materials discovery encompasses chemical plausibility, structural distinctiveness, property relevance and experimental realisability, making AI-driven novelty claims difficult to substantiate. We introduce a materials property hierarchy, from intrinsic, composition-determined properties to extrinsic, processing-dependent performance, to clarify deployment constraints and distinguish structural, physical and deployment novelty. This framework motivates an evidence-based view of multimodal materials data spanning chemical composition, microstructure, processing, and testing and characterisation, showing that current evidence remains concentrated in composition and idealised structure while heterogeneous, under-represented and weakly integrated modalities limit support for physical and deployment novelty. It also highlights the limitations of benchmarks based mainly on computational labels and proxy novelty criteria. Community-wide standards for data collection, modality alignment and evidence synthesis are needed to support multimodal data construction, process-aware multimodal modelling, feasibility-first generative modelling and deployment-aware benchmarking, so that generative and multimodal AI can design experimentally realisable materials with defensible scientific and practical novelty.
