Machine Learning Based Methodology to Predict Point Defect Energies in Multi-Principal Element Alloys

Machine Learning Based Methodology to Predict Point Defect Energies in Multi-Principal Element Alloys

Multi-principal element alloys (MPEAs) are a new class of alloys that consist of many principal elements randomly distributed on a crystal lattice. The random presence of many elements lends large variations in the point defect formation and migration energies even within a given alloy composition....

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Bibliographic Details
Main Authors: Anus Manzoor, Gaurav Arora, Bryant Jerome, Nathan Linton, Bailey Norman, Dilpuneet S. Aidhy
Format: Article
Language:English
Published: Frontiers Media S.A. 2021-06-01
Series:Frontiers in Materials
Subjects:
multi-principal element alloys
machine learning
vacancy migration energies
vacancy formation energies
point defects
Online Access:https://www.frontiersin.org/articles/10.3389/fmats.2021.673574/full

Internet

https://www.frontiersin.org/articles/10.3389/fmats.2021.673574/full

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