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Accurate electronic stopping cross-section (ESCS) database of ions in matter is crucial for precise simulation of radiation damage. Based on the experimental-cleaned database of SRIM, binary theory and unitary convolution approximation as well as the descriptor pool extracted from these models, we developed a universal machine learning ESCS database using the least absolute shrinkage and selection operator (LASSO) algorithm. This method allows for predictions for ion-target combinations with atomic numbers from 1 to 92, within the energy range from 1 keV/u to 1 GeV/u, addressing the limitations of machine learning on training dataset. The database exhibits remarkable accuracy in predicting ESCS and ion depth distribution/range, along with robust reciprocity performance. Key descriptors are also determined, which closely mimic the Lindhard-Scharff-Schiott and Bohr-Bethe-Bloch formulations, achieved

権利情報:

• Creative Commons BY-NC-ND Attribution-NonCommercial-NoDerivs 4.0 International
  

キーワード: electronic stopping cross-sections

刊行年月日: 2024-10-22

出版者: Elsevier BV

掲載誌:

• Nuclear Engineering and Technology (ISSN: 17385733) vol. 57 issue. 3 103271

研究助成金:

• Youth Innovation Promotion Association of the Chinese Academy of Sciences Y202087
• National Natural Science Foundation of China 12375277
• Natural Science Foundation of Anhui Province 2308085J04

原稿種別: 出版者版 (Version of record)

MDR DOI:

公開URL: https://doi.org/10.1016/j.net.2024.10.033

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更新時刻: 2025-12-22 11:12:52 +0900

MDRでの公開時刻: 2025-12-22 12:21:51 +0900