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Can I read Application of Machine Learning in Understanding the Irradiation Damage Mechanism of High-entropy Materials on EtoBox?
Application of Machine Learning in Understanding the Irradiation Damage Mechanism of High-entropy Materials by Shijun Zhao is a Materials Science article available to read on EtoBox.
What is Application of Machine Learning in Understanding the Irradiation Damage Mechanism of High-entropy Materials about?
The concept of high entropy materials (HEMs) provides a fertile ground for developing novel irradiation-resistant structural materials. In HEMs, the vast and complicated configurational space induced by extreme disorder poses grant challenges to understanding defect dynamics and evolution. Machine learning (ML) techniques, which can exploit implicit relationships between diverse descriptors and observations, exhibit great potential in uncovering the governing factors for irradiation damage and modeling local environment dependence of defect dynamics. Herein, three applications of ML in understanding radiation damage in HEMs are summarized and discussed, including ML-based irradiation response prediction, ML-based interatomic potential development, and ML-informed defect evolution.
Who reads Application of Machine Learning in Understanding the Irradiation Damage Mechanism of High-entropy Materials?
It is typically read by researchers, students, and practitioners in Materials Science.
- Author
- Shijun Zhao
- Publisher
- Elsevier BV
- Published
- 2022
- Language
- EN
- Field
- Materials Science (Physical Sciences)