Nuclear Engineering and Technology

Korean Nuclear Society (한국원자력학회)
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Developing an approach for fast estimation of range of ion in interaction with material using the Geant4 toolkit in combination with the neural network

  • Received : 2021.11.10
  • Accepted : 2022.06.04
  • Published : 2022.11.25
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Abstract

Precise modelling of the interaction of ions with materials is important for many applications including material characterization, ion implantation in devices, thermonuclear fusion, hadron therapy, secondary particle production (e.g. neutron), etc. In this study, a new approach using the Geant4 toolkit in combination with the Bayesian regularization (BR) learning algorithm of the feed-forward neural network (FFNN) is developed to estimate the range of ions in materials accurately and quickly. The different incident ions at different energies are interacted with the target materials. The Geant4 is utilized to model the interactions and to calculate the range of the ions. Afterward, the appropriate architecture of the FFNN-BR with the relevant input features is utilized to learn the modelled ranges and to estimate the new ranges for the new cases. The notable achievements of the proposed approach are: 1- The range of ions in different materials is given as quickly as possible and the time required for estimating the ranges can be neglected (i.e. less than 0.01 s by a typical personal computer). 2- The proposed approach can generalize its ability for estimating the new untrained cases. 3- There is no need for a pre-made lookup table for the estimation of the range values.

Keywords

Acknowledgement

The first author would like to express his special gratitude to the research office of the Sharif University of Technology for the support in the present study.

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