Active learning accelerates the discovery of high strength and high ductility lead-free solder alloys-Reference-Cited by-同舟云学术

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Active learning accelerates the discovery of high strength and high ductility lead-free solder alloys

Published:2024-05 Issue: Volume:241 Page:112921
ISSN:0264-1275
Container-title:Materials & Design
language:en
Short-container-title:Materials & Design

Author:

Cao Bin^ORCID,Su Tianhao^ORCID,Yu Shuting,Li Tianyuan,Zhang Taolue,Zhang Jincang,Dong Ziqiang,Zhang Tong-Yi

Publisher

Elsevier BV

Reference54 articles.

1. Domain knowledge-guided interpretive machine learning: formula discovery for the oxidation behavior of ferritic-martensitic steels in supercritical water;Cao;J. Mater. Inform.,2022

2. Discovering a formula for the high temperature oxidation behavior of fecralconi based high entropy alloys by domain knowledge-guided machine learning;Wei;J. Mater. Sci. Technol.,2023

3. Machine learning assisted design of high entropy alloys with desired property;Wen;Acta Mater.,2019

4. Active learning in materials science with emphasis on adaptive sampling using uncertainties for targeted design;Lookman;npj Comput. Mater.,2019

5. Multi-objective optimization in material design and selection;Ashby;Acta Mater.,2000

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Crystallographic phase identifier of a convolutional self-attention neural network (CPICANN) on powder diffraction patterns;IUCrJ;2024-06-27

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