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 <i>ab initio</i> simulation of primary damage and evolution of collision cascades. The model reliability is confirmed by good reproduction of experimentally measured threshold displacement energies and sputtering yields. We find that clustering and recrystallization of radiation-induced defects, propagation pattern of cascades, and coordination defects in the heat spike phase show striking differences to the widely used analytical potentials. The results reveal that small defect clusters are predominant and show new defect structures such as a vacancy surrounded by three interstitials. Quantum-mechanical level of accuracy in simulation of primary damage was achieved by a silicon machine learning potential. The results show quantitative and qualitative differences from the damage predicted by any previous models.

本研究开发了一款适用于辐射效应研究的硅基机器学习高斯近似势能，并首次将其用于碰撞级联初始损伤与演化过程的从头算（ab initio）模拟。该模型的可靠性通过对实验测得的位移阈能与溅射产额的精准复现得到验证。研究发现，辐射诱导缺陷的团簇与再结晶、级联的传播模式以及热尖峰阶段的配位缺陷，均与当前广泛使用的解析势表现出显著差异。结果表明，小尺寸缺陷团簇占据主导地位，且观测到了全新的缺陷结构，例如由三个间隙原子环绕的空位缺陷。本研究所用的硅基机器学习势能实现了初始损伤模拟中的量子力学级精度。本研究结果与以往所有模型预测的损伤结果均存在定量与定性层面的差异。