论文标题
神经网络辅助研究无定形固体水上的氮原子动力学。 I.吸附和解吸
Neural-Network Assisted Study of Nitrogen Atom Dynamics on Amorphous Solid Water. I. Adsorption & Desorption
论文作者
论文摘要
使用分子动力学模拟分析（4S）-n（4S）-n的吸附和解吸动力学。基础势能表面由机器学习的原子间电位提供。结合能证实了最新的可用理论和实验结果。氮粘性系数在10 K的灰尘温度下接近统一，但在较高温度下降低。我们估计在28 K处的解吸时间尺度为1μs。估计的时间尺度允许通过扩散介导的化学过程在解吸前发生，即使在较高的温度下也是如此。我们发现，粘性事件发生后的能量耗散过程发生在10 K的灰尘温度下，即使是用于传入的吸附物的高能量，也会发生。我们的方法允许以负担得起的计算成本和AB-Initio准确性以合理的时间尺度模拟大型系统。此外，它通常适用于尘埃表面上星际自由基的吸附动力学的研究。
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 μs 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.
