Application of Machine Learning to Predict Grain Boundary Embrittlement in Metals by Combining Bonding-Breaking and Atomic Size Effects

Application of Machine Learning to Predict Grain Boundary Embrittlement in Metals by Combining Bonding-Breaking and Atomic Size Effects

The strengthening energy or embrittling potency of an alloying element is a fundamental energetics of the grain boundary (GB) embrittlement that control the mechanical properties of metallic materials. A data-driven machine learning approach has recently been used to develop prediction models to unc...

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Bibliographic Details
Main Authors: Xuebang Wu, Yu-xuan Wang, Kan-ni He, Xiangyan Li, Wei Liu, Yange Zhang, Yichun Xu, Changsong Liu
Format: Article
Language:English
Published: MDPI AG 2020-01-01
Series:Materials
Subjects:
grain boundary embrittlement
machine learning
strengthening energy
support vector machine
artificial neural network
Online Access:https://www.mdpi.com/1996-1944/13/1/179

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https://www.mdpi.com/1996-1944/13/1/179

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