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
Optimal Design of Microwave Absorbing Material of Carbonyl Iron/Ferroferric Oxide Composite via Machine Learning
仲陆祎 1权斌 2车仁超 2陆文聪1
作者信息
- 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
- 折叠
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,并对其中一个样本进行了实验验证.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 are mostly composite materials,which play an important role in resisting electro-magnetic 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,micro wave 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 ab-sorbing properties of microwave absorbing materials,provi-ding new ideas for the optimal design of microwave absorb-ing materials by using machine learning.
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Key words
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引用本文复制引用
基金项目
之江实验室科研攻关项目(2021PE0AC02)
出版年
2024
NETL
NSTL
万方数据