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Energy loss of energetic ions in solid is crucial in many field, and accurateprediction of the ion stopping power is a long-time goal. Though great effortshave been made, it is still very difficult to find a universal prediction modelto accurately calculate the ion stopping power in distinct target materials.Deep learning algorithm is a newly emerged method to solve multi-factorsphysical problems and can mine the deeply implicit relations among parameters,which make it a powerful tool in energy loss prediction. In this work, wedeveloped an energy loss prediction model based on deep learning. Whenexperimental data are available, our model can give predictions with an averageabsolute difference close to 5.7%, which is in the same level compared withother widely used programs e.g. SRIM. In the regime without experimental data,our model still can maintain a high performance, and has higher reliabilitycompared with the existing models. The ion range of Au ions in SiC can becalculated with a relative error of 0.6~25% for ions in the energy range of700~10'000 keV, which is much better than the results calculated by SRIM.Moreover, our model support the reciprocity conjecture of ion stopping power insolid proposed by P. Sigmund, which has been known for a long time but canhardly been proved by any of the existing stopping power models. Thishigh-accuracy energy loss prediction model is very important for the researchof ion-solid interaction mechanism and enormous relevant applications ofenergetic ions, such as in semiconductor fabrications, nuclear energy systemsand the space facilities.

A High Accuracy Electrical Stopping Power Prediction Model based on Deep Learning Algorithm and its Applications