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 - Creator, Publisher

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