期刊
CHINESE PHYSICS B
卷 31, 期 7, 页码 -出版社
IOP Publishing Ltd
DOI: 10.1088/1674-1056/ac4e0c
关键词
electronic stopping power; deep learning; ion range; reciprocity theory
资金
- National Natural Science Foundation of China [12135002, 11705010]
- China Postdoctoral Science Foundation [2019M650351]
- Science Challenge Project [TZ2018004]
In this work, a deep-learning-based stopping power model is developed, overcoming the deficiencies of classical models by improving the predictive accuracy of stopping power for ultra-heavy ion with low energy and the corresponding projected range.
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.
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