摘要
综述了人工智能(artificial intelligence,AI)在材料成分与结构设计、性能预测、合成优化及工业实践中推动材料研发从经验试错向智能设计范式转型的前沿进展。通过融合数据驱动方法、物理嵌入建模与自主实验系统,AI实现了跨尺度性能高精度预测、极端性能材料的逆向设计、合成工艺智能优化及缺陷无损检测等,显著缩短了研发周期并突破了传统试错研发周期长、实验成本高且难以系统逼近材料性能极限等瓶颈。归纳了AI在稳定晶体高效筛选、辐射制冷材料定向开发等典型案例中的利用晶体图神经网络高效筛选大量稳定化合物,以及通过深度生成模型实现性能创纪录的辐射制冷材料逆向设计等突破,阐述了少样本学习、迁移学习及物理机理融合等技术对数据稀缺和多尺度建模等挑战的应对方案。未来,AI将推进材料研发向数据驱动、自主决策和智能迭代的高阶范式加速跃迁。
Artificial intelligence(Al)is profoundly transforming the paradigms and methodologies of advanced materials research and development.This review systematically examines cutting-edge advances in AI applications across materials composition/structure design,property prediction,synthesis optimization,and industrial implementation.By integrating data-driven approaches,physics-informed modeling,and autonomous experimental systems,AI has enabled high-accuracy cross-scale performance prediction,inverse design of materials with extreme properties,intelligent optimization of synthesis processes,and non-destructive defect detection,significantly accelerating development cycles while overcoming performance bottlenecks.The work highlights breakthroughs in representative case studies including high-throughput screening of stable crystals,targeted development of radiative cooling materials,and optimization of electrolytes for high-voltage batteries,while elucidating how techniques such as few-shot learning,transfer learning,and physics-constrained algorithms address challenges in data scarcity and multiscale modeling.Looking forward,the synergistic convergence of AI with quantum computing and generative design will propel materials innovation toward an accelerated transition to advanced paradigms characterized by data-driven workflows,autonomous decision-making,and intelligent iteration.
作者
董樊丽
肖志鹏
李艳辉
DONG Fanli;XIAO Zhipeng;LI Yanhui(School of Materials Science and Engineering,Shanghai Jiao Tong University,Shanghai 200240,China;Inner Mongolia Research Institute,Shanghai Jiao Tong University,Hohhot O10010,China)
出处
《科技导报》
北大核心
2025年第24期35-43,共9页
Science & Technology Review
基金
国家社会科学基金项目(25BJL012)。
关键词
人工智能
材料设计
性能预测
数据驱动
技术融合
artificial intelligence
materials design
property prediction
data-driven
technology integration
