Divide and conquer: Machine learning accelerated design of lead-free solder alloys with high strength and high ductility

作     者：Qinghua Wei Bin Cao Hao Yuan Youyang Chen Kangdong You Shuting Yu Tixin Yang Ziqiang Dong Tong-Yi Zhang 

作者机构：Materials Genome InstituteShanghai UniversityShanghai 200444China Advanced Materials ThrustHong Kong University of Science and Technology(Guangzhou)Guangzhou 511400 GuangdongChina Guangzhou Municipal Key Laboratory of Materials InformaticsGuangzhou 511400 GuangdongChina 

出 版 物：《npj Computational Materials》 (计算材料学（英文）)

年 卷 期：2023年第9卷第1期

页      面：275-284页

核心收录：

学科分类：0402[教育学-心理学(可授教育学、理学学位）] 0303[法学-社会学] 08[工学] 080502[工学-材料学] 0805[工学-材料科学与工程（可授工学、理学学位）] 0802[工学-机械工程] 0701[理学-数学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：This work was sponsored by the National Key Research and Development Program of China(No.2018YFB0704400) Key Program of Science and Technology of Yunnan Province(No.202002AB080001-2) Key Research Project of Zhejiang Laboratory(No.2021PE0AC02) Shanghai Pujiang Program(Grant No.20PJ1403700) Guangzhou Municipal Science and Technology Project(No.2023A03J0003).We would like to acknowledge the support from Yunnan Tin Group(Holding)Co.Ltd,China.We also acknowledge the support from the Shanghai Engineering Research Center for Integrated Circuits and Advanced Display Materials 

主　　题：ductility strength solder 

摘      要：The attainment of both high strength and high ductility is always the goal for structure materials,because the two properties generally are mutually competing,called strength-ductility ***,the data-driven paradigm combined with expert domain knowledge provides the state-of-the-art methodology to design and discovery for structure materials with high strength and high *** enhance both strength and ductility,a joint feature is proposed here to be the product of strength multiplying *** strategy of“divide and conqueris developed to solve the contradictory problem,that material experimental data of mechanical behaviors are,in general,small in size and big in noise,while the design space is huge,by a newly developed data preprocessing algorithm,named the Tree-Classifier for Gaussian Process Regression(TCGPR).The TCGPR effectively divides an original dataset in a huge design space into three appropriate sub-domains and then three Machine Learning(ML)models conquer the three sub-domains,achieving significantly improved prediction accuracy and *** that the Bayesian sampling is applied to design next experiments by balancing exploitation and ***,the experiment results confirm the ML predictions,exhibiting novel lead-free solder alloys with high strength high *** material characterizations were also conducted to explore the mechanism of high strength and high ductility of the alloys.