Using machine learning and feature engineering to characterize limited material datasets of high-entropy alloys
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- 25 February 2020
- journal article
- research article
- Published by Elsevier BV in Computational Materials Science
- Vol. 175, 109618
- https://doi.org/10.1016/j.commatsci.2020.109618
Abstract
No abstract availableKeywords
Funding Information
- National Key Research and Development Program of China (2018YFB0704400)
This publication has 45 references indexed in Scilit:
- High-entropy Alloys with High Saturation Magnetization, Electrical Resistivity and MalleabilityScientific Reports, 2013
- Analysis of phase formation in multi-component alloysJournal of Alloys and Compounds, 2012
- Microstructural control and properties optimization of high-entrop alloysProcedia Engineering, 2012
- Prediction of high-entropy stabilized solid-solution in multi-component alloysMaterials Chemistry and Physics, 2012
- Phase stability in high entropy alloys: Formation of solid-solution phase or amorphous phaseProgress in Natural Science: Materials International, 2011
- Microstructure, thermophysical and electrical properties in AlxCoCrFeNi (0≤x≤2) high-entropy alloysMaterials Science and Engineering B, 2009
- Electrochemical kinetics of the high entropy alloys in aqueous environments—a comparison with type 304 stainless steelCorrosion Science, 2005
- Nanostructured High‐Entropy Alloys with Multiple Principal Elements: Novel Alloy Design Concepts and OutcomesAdvanced Engineering Materials, 2004
- The Problem of OverfittingJournal of Chemical Information and Computer Sciences, 2003
- Quantitative evaluation of critical cooling rate for metallic glassesMaterials Science and Engineering: A, 2001