Development of an Electronic Stopping Power Model based on Deep Learning and its Application in Ion Range Prediction

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

View graph of relations


  • Xun Guo
  • Hao Wang
  • Changkai Li
  • Ke Jin
  • Jianming Xue

Related Research Unit(s)


Original languageEnglish
Article number073402
Journal / PublicationChinese Physics B
Issue number7
Online published24 Jan 2022
Publication statusPublished - Jun 2022


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.

Research Area(s)

  • electronic stopping power, deep learning, ion range, reciprocity theory