Primary damage and electronic effects in Si with machine learning-driven molecular dynamics

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Primary damage and electronic effects in Si with machine learning-driven molecular dynamics - Atomic structure of final defects

Description

This dataset contains the atomic structure of survived defects, induced from self-irradiated silicon with different primary knock-on atom (PKA) energies. The molecular dynamics simulations were performed with the Gaussian approximation machine learning potential, GAP, using the TurboGAP code. In the simulations, a first-principles-derived model for the electronic stopping power was employed. More information about the simulation details is provided in the corresponding README file.
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Year of publication

2025

Authors

Aalto University

Ali Hamedani - Publisher, Creator

Other information

Open access

Open

License

Creative Commons Attribution 4.0 International (CC BY 4.0)

Keywords

Molecular Dynamics, primary radiation damage, machine learning, electronic stopping, GAP potential, m, TurboGAP