Integrating Bayesian Inference with Scanning Probe Experiments for Robust Identification of Surface Adsorbate Configurations
Integrating Bayesian Inference with Scanning Probe Experiments for Robust Identification of Surface Adsorbate Configurations
复制标题
DOI：
10.1002/adfm.202010853
复制
发表时间：
2021-05-13
影响因子：
19
通讯作者：
Rinke, Patrick
中科院分区：
文献类型：
--
作者：
Jarvi, Jari;Alldritt, Benjamin;Rinke, Patrick
控制有机/无机材料的性质需要详细了解它们的分子吸附几何结构。这通常是无法实现的，即使使用当前最先进的工具也是如此。用原子力显微镜(AFM)可视化复杂的非平面吸附的结构是一项具有挑战性的工作，而用传统的结构搜索在计算上识别它是很困难的。在这种全新的方法中，集成了跨学科工具，以稳健和自动地识别3D吸附组态。采用贝叶斯优化与第一性原理模拟相结合的方法，对多组分吸附质进行了准确、无偏倚的结构推断。然后，相应的AFM模拟允许对AFM实验图像中出现的吸附结构进行指纹识别。在大体积(1S)-樟脑吸附在铜(111)表面的情况下，发现了三个匹配的AFM图像对比，这使得实验图像特征与分子吸附的不同情况相关联。
Controlling the properties of organic/inorganic materials requires detailed knowledge of their molecular adsorption geometries. This is often unattainable, even with current state-of-the-art tools. Visualizing the structure of complex non-planar adsorbates with atomic force microscopy (AFM) is challenging, and identifying it computationally is intractable with conventional structure search. In this fresh approach, cross-disciplinary tools are integrated for a robust and automated identification of 3D adsorbate configurations. Bayesian optimization is employed with first-principles simulations for accurate and unbiased structure inference of multiple adsorbates. The corresponding AFM simulations then allow fingerprinting adsorbate structures that appear in AFM experimental images. In the instance of bulky (1S)-camphor adsorbed on the Cu(111) surface, three matching AFM image contrasts are found, which allow correlating experimental image features to distinct cases of molecular adsorption.