数据与模型驱动的钙钛矿材料智能计算框架  

Data and Model Driven Intelligent Computing Framework for Perovskite Materials

作　　者：程晋荣[1] 何鹏飞 李艺欣 雷咏梅[2] CHENG Jinrong;HE Pengfei;LI Yixin;LEI Yongmei(School of Materials Science and Engineering,Shanghai University,Shanghai 200444,China;School of Computer Engineering and Science,Shanghai University,Shanghai 200444,China)

机构地区：[1]上海大学材料科学与工程学院,上海200444 [2]上海大学计算机工程与科学学院,上海200444

出　　处：《中国材料进展》2025年第4期309-318,共10页Materials China

基　　金：国家自然科学基金资助项目(52472133,91427304);上海市自然科学基金原创探索项目(22ZR1481100);水声对抗技术重点实验室开放基金资助项目(JCKY2024207CH12);中国博士后科学基金资助项目(2024M751931)。

摘　　要：钙钛矿材料因其复杂的化学成分、多样的晶体结构和丰富的物理特性,成为现代材料科学研究热点之一。结合模型驱动方法和数据驱动方法,构建特征工程融合主动学习的材料智能计算框架,提高模型精度和系统性能。通过数据布局和动态调度协同优化,提出针对材料特征的确定独立筛选和稀疏算子(SISSO)并行计算方法,缓解SISSO算法在建立特征工程模型时面临的精度较低与计算成本较高的问题,降低数据质量对模型的影响。构建面向材料数据的主动学习方法,以处理材料数据标记的复杂性,剔除噪声数据。Perovskite materials have become one of the hotspots in modern materials science research due to their complex chemical compositions,diverse crystal structures and rich physical properties.In this paper,by combining the model-driven approach and the data-driven approach,a materials intelligent computing framework integrating feature engineering and active learning is constructed to improve the model accuracy and system performance.Through the collaborative optimization of data layout and dynamic scheduling,a sure independence screening and sparsifying operator(SISSO)parallel computing method for material features is proposed to alleviate the problems of low accuracy and high computational cost faced by the SISSO algorithm when establishing the feature engineering model and reduce the impact of data quality on the model.An active learning method oriented to material data is constructed to deal with the complexity of material data labeling and eliminate noisy data.

关 键 词：SISSO算法 智能计算 主动学习 钙钛矿材料 

分 类 号：TQ174.1[化学工程—陶瓷工业] TP181[化学工程—硅酸盐工业]