• Corpus ID: 251223412

Predicting electronic stopping powers using stacking ensemble machine learning method

@inproceedings{Akbari2022PredictingES,
  title={Predicting electronic stopping powers using stacking ensemble machine learning method},
  author={Fatemeh Akbari and Somayeh Taghizadeh and Diana Shvydka and Nicholas Niven Sperling and E. Ishmael Parsai},
  year={2022}
}
Background: Accurate electronic stopping power data is crucial for calculations of radiation-induced effects in a wide range of applications, from dosimetry and radiotherapy to particle physics. The data is dependent on the parameters of both the incident charged particle and the stopping medium. The existent Bethe theory can be used to calculate the stopping power of high-energy ions, but fails at lower energies, leaving incomplete and even contradictory experimental data, often expanded… 

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