Review on Perovskite-Type Compound Using Machine Learning

被引:1
|
作者
Zhang, Taohong [1 ,4 ]
Guo, Xueqiang [1 ,4 ]
Zheng, Han [2 ]
Liu, Yun [3 ]
Wulamu, Aziguli [1 ,4 ]
Chen, Han [1 ,4 ]
Guo, Xuxu [1 ,4 ]
Zhang, Zhizhuo [5 ]
机构
[1] Univ Sci & Technol Beijing USTB, Sch Comp & Commun Engn, Dept Comp, Beijing 100083, Peoples R China
[2] HeChi Univ, Educ Dept Guangxi Zhuang Autonomous Reg, Key Lab AI & Informat Proc, Hechi 546300, Guangxi, Peoples R China
[3] Beijing Haidian Hosp, Dept Tradit Chinese Med, Beijing 100080, Peoples R China
[4] Beijing Key Lab Knowledge Engn Mat Sci, Beijing 100083, Peoples R China
[5] Univ Sci & Technol Beijing USTB, Sch Econ & Management, Beijing 100083, Peoples R China
关键词
Machine Learning; Perovskite; Feature Engineer; Deep Learning; SOLAR-CELLS; ABSORPTION; REGRESSION; EFFICIENCY; SCIENCE; OXIDES; MODEL;
D O I
10.1166/sam.2022.4302
中图分类号
TB3 [工程材料学];
学科分类号
0805 ; 080502 ;
摘要
Perovskite is a kind of promising class of materials nowadays because of its exciting performance in energy, catalysis, semiconductor, and many other areas. Machine learning is a potential method by using big data to mine the deep hidden laws of the data and make some predictions of the new data. Applying machine learning method in perovskite is a meaningful attempt to explore the new material with new properties and to predict the properties of new materials. This review shows recent progress of perovskite using machine learning, and these attempts show the success of combining big data technique and material science which give us the new direction to explore the application of machine learning method and the new tools for material science.
引用
收藏
页码:1001 / 1017
页数:17
相关论文
共 50 条
  • [1] The Development of New Perovskite-Type Oxygen Transport Membranes Using Machine Learning
    Schlenz, Hartmut
    Baumann, Stefan
    Meulenberg, Wilhelm Albert
    Guillon, Olivier
    CRYSTALS, 2022, 12 (07)
  • [2] Review of magnetocaloric effect in perovskite-type oxides
    钟伟
    区泽棠
    都有为
    Chinese Physics B, 2013, 22 (05) : 28 - 38
  • [3] Review of magnetocaloric effect in perovskite-type oxides
    Zhong Wei
    Au Chak-Tong
    Du You-Wei
    CHINESE PHYSICS B, 2013, 22 (05)
  • [4] A Review on the Catalytic Decomposition of NO by Perovskite-Type Oxides
    Shen, Qiuwan
    Dong, Shuangshuang
    Li, Shian
    Yang, Guogang
    Pan, Xinxiang
    CATALYSTS, 2021, 11 (05)
  • [5] Exceptional Dielectric Phase Transitions in a Perovskite-Type Cage Compound
    Zhang, Wen
    Cai, Ying
    Xiong, Ren-Gen
    Yoshikawa, Hirofumi
    Awaga, Kunio
    ANGEWANDTE CHEMIE-INTERNATIONAL EDITION, 2010, 49 (37) : 6608 - 6610
  • [6] HIGH PRESSURE STUDIES ON THE PEROVSKITE-TYPE COMPOUND BaBiO3.
    Sugiura, H.
    Yamadaya, T.
    Physica B: Physics of Condensed Matter & C: Atomic, Molecular and Plasma Physics, Optics, 1985, 139-140 : 349 - 352
  • [7] Thermal energy storage in a 3D perovskite-type compound
    Chen, Shaoli
    Zhang, Weixiong
    Chen, Xiaoming
    ACTA CRYSTALLOGRAPHICA A-FOUNDATION AND ADVANCES, 2017, 73 : C1009 - C1009
  • [8] HIGH-PRESSURE STUDIES ON THE PEROVSKITE-TYPE COMPOUND BABIO3
    SUGIURA, H
    YAMADAYA, T
    PHYSICA B & C, 1986, 139 (1-3): : 349 - 352
  • [9] Application of the SHS technique in the synthesis of the perovskite-type MgxCyNi3 compound
    Ferretti, M
    Ciccarelli, C
    Magnone, E
    Rubino, S
    Parodi, N
    Martinelli, A
    MATERIALS RESEARCH BULLETIN, 2004, 39 (4-5) : 647 - 654
  • [10] Machine learning for perovskite optoelectronics:a review
    Feiyue Lu
    Yanyan Liang
    Nana Wang
    Lin Zhu
    Jianpu Wang
    Advanced Photonics, 2024, 6 (05) : 22 - 34