[1]王向东,徐鹏程,卢天,等.低电阻率三元金合金材料的逆向设计[J].中国材料进展,2021,40(04):251-256.[doi:10.7502/j.issn.1674-3962.202010008]
 WANG Xiangdong,XU Pengcheng,LU Tian,et al.Inverse Design of Ternary Gold Alloy Materials with Low Resistivity[J].MATERIALS CHINA,2021,40(04):251-256.[doi:10.7502/j.issn.1674-3962.202010008]
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低电阻率三元金合金材料的逆向设计()
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中国材料进展[ISSN:1674-3962/CN:61-1473/TG]

卷:
40
期数:
2021年第04期
页码:
251-256
栏目:
出版日期:
2021-04-30

文章信息/Info

Title:
Inverse Design of Ternary Gold Alloy Materials with Low Resistivity
文章编号:
1674-3962(2021)04-0251-06
作者:
王向东1徐鹏程1卢天1刘秀娟2陆文聪12
(1.上海大学 材料基因组工程研究院,上海 200444)(2.上海大学理学院,上海 200444)
Author(s):
WANG Xiangdong1XU Pengcheng1LU Tian1LIU Xiujuan2 LU Wencong12
(1. Materials Genome Institute,Shanghai University,Shanghai 200444,China) (2. College of Sciences,Shanghai University,Shanghai 200444,China)
关键词:
模式识别最佳投影识别三元金合金材料电阻率机器学习
Keywords:
pattern recognition optimal projection recognition ternary gold alloy materials resistivity machinelearning
分类号:
TG146.3+1;TG132.2+1;TP181
DOI:
10.7502/j.issn.1674-3962.202010008
文献标志码:
A
摘要:
金及其合金特别是三元金合金在电接触材料领域得到了广泛的应用。由于三元金合金组分和配比的复杂性,如何高效地设计具有低电阻率的三元金合金电接触材料仍然是一个挑战。提出了一种低电阻率三元金合金材料的逆向设计方法,该方法将机器学习的定性分类方法(模式识别最佳投影)与定量预测方法(XGBoost)相结合,设计出比已有三元金合金材料电阻率更低的新材料。采用最大相关最小冗余(mRMR)结合XGBoost算法筛选出建模的特征变量;利用模式识别逆投影方法设计了3个低电阻率三元金合金候选样本,即AuZr1.95Cu0.52、AuZr1.12Cu4和AuSc1.86Cu2.75,并通过XGBoost模型估算了候选样本的电阻率。结果表明,根据模式识别逆投影方法设计的样本具有较低的电阻率,其电阻率负对数(-lg ρ)预报值分别为6.718,6.707和6.701,均超过了原始数据集-lg ρ的最大值6.68。该研究方法作为材料逆向设计的参考方法,有助于实验数据的统计规律挖掘,可以加快新材料设计。
Abstract:
Gold and its alloys, especially ternary gold alloys, have been widely used in the field of electrical contact materials. Due to the complicated components and ratios of ternary gold alloy, how to efficiently design electrical contact materials of ternary gold alloy with low resistivity is still a challenge. In this work, new ternary gold alloy materials with lower resistivity were designed based on the inverse design method combining qualitive method (optimal projection of pattern recognition) with quantitative method (XGBoost). The critical features were screened out by using the maximum relevant minimum redundancy (mRMR) integrated with the XGBoost algorithm. Three candidate samples with lower resistivity, i.e., AuZr1.95Cu0.52,AuZr1.12Cu4 and AuSc1.86Cu2.75 were designed by using the inverse projection of pattern recognition method developed in our laboratory, and the resistivity of the candidate samples was estimated by the XGBoost model. The results indicate that the predicted negative logarithms (-lg ρ) of designed samples are 6.718, 6.707 and 6.701, respectively, exceeding the maximum value of 6.68 in the original data set. As a reference method for material inverse design, this research method is helpful for mining the statistical regularities in experimental data, and can accelerate the design of new materials.

备注/Memo

备注/Memo:
收稿日期:2020-10-22修回日期:2020-11-22 基金项目:国家重点研究发展计划项目(2016YFB0700504);上海市国际科技合作基金项目(18520723500)第一作者:王向东,男,1995年生,硕士研究生通讯作者:陆文聪,男,1964年生,教授,博士生导师, Email: wclu@shu.edu.cn
更新日期/Last Update: 2021-03-24