Insights into the primary radiation damage of silicon by a machine learning interatomic potential
We develop a silicon Gaussian approximation machine learning potential suitable for radiation effects, and use it for the first ab initio simulation of primary damage and evolution of collision cascades. The model reliability is confirmed by good reproduction of experimentally measured threshold dis...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2020-10-01
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| Series: | Materials Research Letters |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.1080/21663831.2020.1771451 |