Impact Factor:19.0
DOI number:10.1002/adfm.202109367
Affiliation of Author(s):中南大学粉末冶金研究院, 中南大学交通运输工程学院，南洋理工大学机械与航天工程学院
Teaching and Research Group:高温结构材料研究所，高速列车研究中心
Journal:Advanced Functional Materials
Funded by:中国国家重点研究与发展计划（2016YFB0701404），中国国家自然科学基金（NSFC）（91860105）&（52074366），中国博士后科学基金会（2019M662799），中南大学创新驱动计划的青年人才项目（2019XZ027），中国山东主要科技创新项目（2019JZZY010325），长沙市自然科学基金（kq2014126）
Key Words:Alloy design, Creep resistance, High-throughput methods, Microstructure stability, Unsupervised machine learning
Abstract:Ever-increasing demands for superior alloys with improved high-temperature service properties require accurate design of their composition. However, conventional approaches to screen the properties of alloys such as creep resistance and microstructural stability cost a lot of time and resources. This work therefore proposes a novel high throughput-based design strategy for high-temperature alloys to accelerate their composition selections, by taking Ni-based superalloys as an example. A numerical inverse method is used to massively calculate the multielement diffusion coefficients based on an accurate atomic mobility database. These coefficients are subsequently employed to refine the physical models for tuning the creep rates and structural stability of alloys, followed by unsupervised machine learning to categorize their composition and determine the range of the composition with optimal performance. By using a strict screening criterion, two sets of composition with comprehensively optimal properties are selected, which is then validated by experiments. Compared with recent data-driven methods for materials design, this strategy exhibits high accuracy and efficiency attributed to the high-throughput multicomponent diffusion couples, self-developed atomic mobility database, and refined physical models. Since this strategy is independent of the alloy composition, it can efficiently accelerate the development of multicomponent high-performance alloys and tackle challenges in discovering novel materials.
Co-author:Zexin Wang, Zi Wang, Jing Zhong, Lei Zhao, Liang Jiang, Runhua Zhou, Yong Liu, Yujia Tian, Han Zheng, Qihong Fang, Lijun Zhang, Lina Zhang, Hong Wu
First Author:Feng Liu
Indexed by:Article
Correspondence Author:Lan Huang, Liming Tan, Lichun Bai, Kun Zhou
Document Code:2109367
Discipline:Engineering
First-Level Discipline:冶金
Volume:32
Issue:28
ISSN No.:1616-301X
Translation or Not:no