Development of an electronic stopping power model based on deep learning and its application in ion range prediction

Xun Guo, Hao Wang, Changkai Li, Shijun Zhao, Ke Jin*, Jianming Xue*

*此作品的通讯作者

科研成果: 期刊稿件文章同行评审

6 引用 (Scopus)

摘要

Deep learning algorithm emerges as a new method to take the raw features from large dataset and mine their deep implicit relations, which is promising for solving traditional physical challenges. A particularly intricate and difficult challenge is the energy loss mechanism of energetic ions in solid, where accurate prediction of stopping power is a long-time problem. In this work, we develop a deep-learning-based stopping power model with high overall accuracy, and overcome the long-standing deficiency of the existing classical models by improving the predictive accuracy of stopping power for ultra-heavy ion with low energy, and the corresponding projected range. This electronic stopping power model, based on deep learning algorithm, could be hopefully applied for the study of ion-solid interaction mechanism and enormous relevant applications.

源语言英语
文章编号073402
期刊Chinese Physics B
31
7
DOI
出版状态已出版 - 1 6月 2022

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Guo, X., Wang, H., Li, C., Zhao, S., Jin, K., & Xue, J. (2022). Development of an electronic stopping power model based on deep learning and its application in ion range prediction. Chinese Physics B, 31(7), 文章 073402. https://doi.org/10.1088/1674-1056/ac4e0c