文章信息/Info

Title:

Data and Model Driven Intelligent Computing Framework for Perovskite Materials

文章編號:

1674-3962(2025)04-0309-09

作者:

程晉榮; 何鵬飛; 李藝欣; 雷詠梅

1.上海大學材料科學與工程學院 ，上海 200444 2.上海大學計算機工程與科學學院，上海 200444

Author(s):

CHENG Jinrong;  HE Pengfei;  LI Yixing;  LEI Yongmei

1.School of Materials Science and Engineering, Shanghai University, Shanghai 200444, China 2.School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China

關鍵詞:

SISSO算法; 智能計算; 主動學習; 鈣鈦礦材料

Keywords:

SISSO algorithm;  intelligent computing;  active learning;  perovskite materials

分類號:

TQ174.1; TP181

DOI:

10.7502/j.issn.1674-3962.202412002

文獻標志碼:

A

摘要:

鈣鈦礦材料因其復雜的化學成分、多樣的晶體結構和豐富的物理特性，成為現代材料科學研究熱點之一。結合模型驅動方法和數據驅動方法，構建特征工程融合主動學習的材料智能計算框架，提高模型精度和系統性能。通過數據布局和動態調度協同優化，提出針對材料特征的確定獨立篩選和稀疏算子（SISSO）并行計算方法，緩解SISSO算法在建立特征工程模型時面臨的精度較低與計算成本較高的問題，降低數據質量對模型的影響。構建面向材料數據的主動學習方法，以處理材料數據標記的復雜性，剔除噪聲數據。

Abstract:

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�瞕riven 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.