Interpretable machine learning for stability and electronic structure prediction of Janus III–VI van der Waals heterostructures

Interpretable machine learning for stability and electronic structure prediction of Janus III–VI van der Waals heterostructures

Abstract Machine learning (ML) techniques have made enormous progress in the field of materials science. However, many conventional ML algorithms operate as “black‐boxes”, lacking transparency in revealing explicit relationships between material features and target properties. To address this, the d...

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Bibliographic Details
Main Authors: Yudong Shi, Yinggan Zhang, Jiansen Wen, Zhou Cui, Jianhui Chen, Xiaochun Huang, Cuilian Wen, Baisheng Sa, Zhimei Sun
Format: Article
Language:English
Published: Wiley-VCH 2024-12-01
Series:Materials Genome Engineering Advances
Subjects:
descriptor
interpretable machine learning
Janus III–VI van der Waals heterostructures
Online Access:https://doi.org/10.1002/mgea.76

Internet

https://doi.org/10.1002/mgea.76

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