Dynamics of adsorption and desorption of (4S)-N on amorphous solid water are analyzed using molecular dynamics simulations. The underlying potential energy surface was provided by machine-learned interatomic potentials. Binding energies confirm the latest available theoretical and experimental results. The nitrogen sticking coefficient is close to unity at dust temperatures of 10 K but decreases at higher temperatures. We estimate a desorption time scale of 1 {\mu}s at 28 K. The estimated time scale allows chemical processes mediated by diffusion to happen before desorption, even at higher temperatures. We found that the energy dissipation process after a sticking event happens on the picosecond timescale at dust temperatures of 10 K, even for high energies of the incoming adsorbate. Our approach allows the simulation of large systems for reasonable time scales at an affordable computational cost and ab-initio accuracy. Moreover, it is generally applicable for the study of adsorption dynamics of interstellar radicals on dust surfaces.

中文翻译：

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无定形固体水中氮原子动力学的神经网络辅助研究。一、吸附解吸

使用分子动力学模拟分析了 (4S)-N 在无定形固体水中的吸附和解吸动力学。潜在的势能面由机器学习的原子间势提供。结合能证实了最新可用的理论和实验结果。氮粘附系数在 10 K 的粉尘温度下接近统一，但在更高的温度下会降低。我们估计 28 K 时的解吸时间尺度为 1 {\mu}s。估计的时间尺度允许由扩散介导的化学过程在解吸之前发生，即使在更高的温度下也是如此。我们发现粘附事件后的能量耗散过程发生在 10 K 尘埃温度的皮秒时间尺度上，即使对于进入的吸附质的高能量也是如此。我们的方法允许以可承受的计算成本和 ab-initio 精度在合理的时间尺度内模拟大型系统。此外，它普遍适用于星际自由基在尘埃表面的吸附动力学研究。