Primary radiation damage in Si1−xGex alloys: Molecular dynamics study with machine-learning interatomic potential

Huyang Li; Xiangli Meng; Yuhang Jing; Lingzhi Cong; Xintong Zhang; Junqing Zhao; Yi Sun; Weiqi Li; Jihong Yan; Jianqun Yang; Xingji Li

doi:10.1016/j.commatsci.2024.113484

JOURNAL ARTICLE

Primary radiation damage in Si1−xGex alloys: Molecular dynamics study with machine-learning interatomic potential

Huyang Li Xiangli Meng Yuhang Jing Lingzhi Cong Xintong Zhang Junqing Zhao Yi Sun Weiqi Li Jihong Yan Jianqun Yang Xingji Li

Year: 2024 Journal: Computational Materials Science Vol: 246 Pages: 113484-113484 Publisher: Elsevier BV

DOI: 10.1016/j.commatsci.2024.113484

Get Full-Text PDF Get Analytical Report

Keywords:

Interatomic potential Molecular dynamics Radiation damage Primary (astronomy) Materials science Dynamics (music) Radiation Chemical physics Chemistry Physics Computational chemistry Nuclear physics Quantum mechanics

Metrics

Cited By

0.17

FWCI (Field Weighted Citation Impact)

Refs

0.44

Citation Normalized Percentile

Is in top 1%

Is in top 10%

Citation History

Topics

Machine Learning in Materials Science

Physical Sciences → Materials Science → Materials Chemistry

Nuclear Materials and Properties

Physical Sciences → Materials Science → Materials Chemistry

Ion-surface interactions and analysis

Physical Sciences → Engineering → Computational Mechanics

Primary radiation damage in Si1−xGex alloys: Molecular dynamics study with machine-learning interatomic potential

Metrics

Citation History

Topics

Related Documents

A machine-learning interatomic potential to understand primary radiation damage of silicon

Atomistic Structures of Amorphous Si1-xGex Alloys: A Molecular-Dynamics Study

Molecular dynamics study on magnesium hydride nanoclusters with machine-learning interatomic potential

Primary radiation damage in silicon from the viewpoint of a machine learning interatomic potential

Insights into the primary radiation damage of silicon by a machine learning interatomic potential