摘要
随着人工智能技术的不断进步,材料数据库在材料科学研究中扮演着日益重要的角色。旨在探讨材料数据库如何通过与AI技术的融合,扩展其应用范围并提升其核心价值。通过文献综述的方法,系统地分析了材料数据库的当前分类,包括材料基础数据库、生产加工数据库、应用服役数据库等,并概述了支撑技术如机器学习、深度学习、数据标准化技术的应用情况。尽管国际上材料数据库的发展呈现出智能化、网络化、资产化、去中心化的趋势,但在数据质量、数据共享、知识产权、市场运维等方面仍面临挑战。未来材料数据库的发展将受益于与新兴技术如材料数据工厂、区块链、隐私计算、AI大模型的结合,这将为新材料的研发和应用提供创新的手段和场景工具。
With the continuous advancement of artificial intelligence(AI)technology,material databases are increasingly playing a pivotal role in materials science research.This paper aims to explore how the integration of AI technology can expand the application scope and enhance the core value of material databases.Through a literature review,the current classifications of material databases were systematically analyzed,including material basic databases,production and processing databases,and application service databases,and outlined the application of supporting technologies such as machine learning,deep learning,and data standardization.Despite the international development of material databases showing trends towards intelligence,networking,assetization,and decentralization,challenges remain in terms of data quality,data sharing,intellectual property rights,and market operation and maintenance.The future development of material databases is expected to benefit from the integration with emerging technologies such as material data factories,blockchain,privacy computing,and AI large models,which will provide innovative means and tools for the research and development and application of new materials.
作者
冯建发
王畅畅
苏航
宿彦京
FENG Jianfa;WANG Changchang;SU Hang;SU Yanjing(Institute for Advanced Materials and Technology,University of Science and Technology Beijing,Beijing 100083,China;Beijing MatDao Technology Co.,Ltd.,Beijing 100081,China;Material Digital R&D Center,China Iron&Steel Research Institute Group,Beijing 100081,China)
出处
《中国材料进展》
北大核心
2026年第2期89-101,共13页
Materials China
基金
国家重点研发计划项目(2022YFB3505202)。
关键词
材料数据库
大数据技术
AI
机器学习
大模型
material database
big data technology
AI
machine learning
large model
