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Chin. Phys. B, 2022, Vol. 31(7): 073402    DOI: 10.1088/1674-1056/ac4e0c

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

Xun Guo(郭寻)1, Hao Wang(王浩)2, Changkai Li(李长楷)2, Shijun Zhao(赵仕俊)3, Ke Jin(靳柯)1,†, and Jianming Xue(薛建明)4,‡
1 Advanced Research Institute of Multidisciplinary Science, Beijing Institute of Technology, Beijing 100081, China;
2 State Key Laboratory of Nuclear Physics and Technology, School of Physics, Peking University, Beijing 100871, China;
3 Department of Mechanical Engineering, City University of Hong Kong, Hong Kong, China;
4 Center for Applied Physics and Technology, Ministry of Education Key Laboratory of High Energy Density Physics Simulations, and Peking University Branch of Ministry of Education IFSA Center, Peking University, Beijing 100871, China
Abstract  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.
Keywords:  electronic stopping power      deep learning      ion range      reciprocity theory  
Received:  07 December 2021      Revised:  19 January 2022      Accepted manuscript online:  24 January 2022
PACS:  34.10.+x (General theories and models of atomic and molecular collisions and interactions (including statistical theories, transition state, stochastic and trajectory models, etc.))  
  34.50.Bw (Energy loss and stopping power)  
  07.05.Mh (Neural networks, fuzzy logic, artificial intelligence)  
Fund: Project supported by the National Natural Science Foundation of China (Grant Nos. 12135002 and 11705010), the China Postdoctoral Science Foundation (Grant No. 2019M650351), and the Science Challenge Project (Grant No. TZ2018004).
Corresponding Authors:  Ke Jin, Jianming Xue     E-mail:;

Cite this article: 

Xun Guo(郭寻), Hao Wang(王浩), Changkai Li(李长楷),Shijun Zhao(赵仕俊), Ke Jin(靳柯), and Jianming Xue(薛建明) Development of an electronic stopping power model based on deep learning and its application in ion range prediction 2022 Chin. Phys. B 31 073402

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