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

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 longtime 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.