论文标题
用于表面化学分析的原子散射
Atomic Scattering For Chemical Analysis Of Surfaces
论文作者
论文摘要
该研究探索了用于揭示氦自旋回波光谱中化学敏感性的机器学习方法，以获得超敏感的表面分析技术。我们对双种体进行了建模，并证明，通过使用深层学习的神经网络，可以获得部分表面浓度。质量50和100上午100点的颗粒系统的示例系统进行了测试，并具有特征性的吸附物和吸附物 - 底物相互作用，部分表面浓度可分解高达20％的吸附位点占用率，而适度高的噪声水平为4％。
The study explores machine learning methods for revealing chemical sensitivity in Helium spin-echo spectroscopy, in order to obtain ultra-sensitive surface analytic technique. We model bi-species co-adsorbed systems and demonstrate that by using deep-learning neural-networks partial surface concentrations are obtainable. An example system of particles with mass 50 and 100 a.m.u was tested with characteristic inter-adsorbate and adsorbate-substrate interactions, with partial surface concentrations being resolvable up to 20% occupancy of adsorption sites, and with modestly high noise level of 4%.
