[1]仲陆祎,权 斌,车仁超,等.基于机器学习的羰基铁/四氧化三铁复合吸波材料的优化设计[J].中国材料进展,2024,43(07):010-19.[doi:10.7502/j.issn.1674-3962.202209040]
 ZHONG Luyi,QUAN Bin,CHE Renchao,et al.Optimal Design of Microwave Absorbing Material of Carbonyl iron/Ferroferric Oxide Composite via Machine Learning[J].MATERIALS CHINA,2024,43(07):010-19.[doi:10.7502/j.issn.1674-3962.202209040]
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基于机器学习的羰基铁/四氧化三铁复合吸波材料的优化设计()
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中国材料进展[ISSN:1674-3962/CN:61-1473/TG]

卷:
43
期数:
2024年第07期
页码:
010-19
栏目:
出版日期:
2024-07-30

文章信息/Info

Title:
Optimal Design of Microwave Absorbing Material of Carbonyl iron/Ferroferric Oxide Composite via Machine Learning
作者:
仲陆祎1权 斌2车仁超2陆文聪1
1. 上海大学 理学院化学系,上海 200444 2. 复旦大学 先进材料实验室,上海 200433
Author(s):
ZHONG Luyi QUAN Bin CHE Renchao LU Wencong
1. Department of Chemistry, College of Sciences, Shanghai University, Shanghai 200444, China2. Advanced Materials Laboratory, Fudan University, Shanghai 200433, China
关键词:
吸波材料磁导率实部积分值磁导率虚部积分值机器学习高通量筛选
Keywords:
microwave absorbing material the integral value of the real part of permeability the integral value of the imaginary part of permeability machine learning high-throughput screening
分类号:
TP181; TB34
DOI:
10.7502/j.issn.1674-3962.202209040
文献标志码:
A
摘要:
吸波材料多为复合材料,在抵御电磁干扰和电磁辐射等方面发挥着重要作用,其磁导率与材料对磁能的储存和消耗以及材料的阻抗匹配有关。以羰基铁/四氧化三铁复合吸波材料为研究对象、6个球磨工艺参数为特征变量,分别运用随机森林回归(random forest regression,RFR)算法和支持向量回归(support vector regression,SVR)算法,构建了磁导率实部积分值和虚部积分值的机器学习模型。通过两步高通量筛选,设计了3个兼顾磁导率实部积分值和虚部积分值的虚拟样本,并对其进行了实验验证。结果表明,磁导率实部积分值和虚部积分值的相对预测误差分别为3.14%和-6.56%。该研究方法能够挖掘工艺参数和材料吸波性能之间的关系,加快新材料的研发,为运用机器学习优化设计吸波材料提供了思路。
Abstract:
Microwave absorbing materials (MAM) are mostly composite materials, which play an important role in resisting electromagnetic interference and electromagnetic radiation. Their permeability is related to the storage and consumption of magnetic energy, as well as the impedance matching of the materials. In this work, microwave absorbing material of carbonyl iron/ferroferric oxide composite is taken as the research object, the machine learning models of the integral value of the real part of permeability (∫μ′) and the integral value of the imaginary part of permeability (∫μ′′) are constructed by using random forest regression (RFR) algorithm and support vector regression (SVR) algorithm respectively, with six ball milling process parameters as feature variables. Three virtual samples are designed by two-step high-throughput screening, considering both ∫μ′ and ∫μ′′, and the experimental verification is carried out. The results show that the relative prediction error of ∫μ′ is 3.14%, and the relative prediction error of ∫μ′′ is -6.56%. Therefore, this research method can be used to explore the relationship between process parameters and absorbing properties of MAM, accelerating the research and development of new materials to provide ideas for the optimal design of microwave absorbing materials by using machine learning

参考文献/References:

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备注/Memo

备注/Memo:
基金项目:国家自然科学基金资助项目(59493300);教育部博士点基金资助项目(9800462)_____________________ 收稿日期: 2000-03-11;修订日期:2000-03-06
作者简介: 张 涌(1967—),男,陕西西安人,北京航空航天大学教授,博士. ----小5号宋体 指代不明
更新日期/Last Update: 2024-06-26