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

Kuvaus

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.

Näytä enemmän

Julkaisuvuosi

2025

Aineiston tyyppi

Tekijät

Aalto-yliopisto

Ali Hamedani - Tekijä, Julkaisija

Projekti

Muut tiedot

Tieteenalat

Kieli

Saatavuus

Avoin

Lisenssi

Creative Commons Nimeä 4.0 Kansainvälinen (CC BY 4.0)

Avainsanat

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

Asiasanat

Ajallinen kattavuus

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