A reverse design model for high-performance and low-cost magnesium alloys by machine learning
Authors:
- Xiaoxi MiXMXiaoxi Mi
- Lianjuan TianLTLianjuan Tian
- Aitao TangATAitao Tang
- Jing KangJKJing Kang
- Peng PengPPPeng Peng
- Jia SheJSJia She
- Hailian WangHWHailian Wang
- Xianhua ChenXCXianhua Chen
- Fusheng PanFPFusheng Pan
- 1 January 2022
- journal article
- research article
- Published by Elsevier in Computational Materials Science
- Vol. 201
Abstract
No abstract availableKeywords
Funding Information
- National Natural Science Foundation of China
This publication has 35 references indexed in Scilit:
- Data mining our way to the next generation of thermoelectricsScripta Materialia, 2016
- High strength and superior ductility of an ultra-fine grained magnesium–manganese alloyMaterials Science and Engineering: A, 2015
- Deep learningNature, 2015
- High-speed extrusion of heat-treatable Mg–Al–Ca–Mn dilute alloyScripta Materialia, 2015
- Microstructure and mechanical properties of asextruded Mg–xAl–5Sn–0·3Mn alloys (x = 1, 3, 6 and 9)Materials Science and Technology, 2015
- Deep learning in neural networks: An overviewNeural Networks, 2015
- Microstructure evolution and mechanical properties of Mg-x%Zn-1%Mn (x=4, 5, 6, 7, 8, 9) wrought magnesium alloysTransactions of Nonferrous Metals Society of China, 2011
- Microstructures and mechanical properties of high strength Mg-Zn-Mn alloyTransactions of Nonferrous Metals Society of China, 2008
- Development of a New Wrought Magnesium-Aluminum-Manganese Alloy AM30Metallurgical and Materials Transactions A, 2007
- Evolving responsively: adaptive mutationNature Reviews Genetics, 2001