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arXiv 2104.05302cond-mat.mtrl-sci
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结合贝叶斯推断与扫描探针实验的表面吸附物稳健识别

Integrating Bayesian Inference with Scanning Probe Experiments for Robust Identification of Surface Adsorbate Configurations

Jari Järvi, Benjamin Alldritt, Ondřej Krejčí, Milica Todorović, Peter Liljeroth, Patrick Rinke

摘要:

控制有机/无机材料的性质需要详细了解其分子吸附几何形状。这通常是无法实现的,即使使用当前最先进的工具。用原子力显微镜(AFM)可视化复杂非平面吸附物的结构是具有挑战性的,并且用传统的结构搜索通过计算来识别它是棘手的。在这种新的方法中,跨学科的工具被集成为一个强大的和自动化的3D吸附物配置的识别。贝叶斯优化与第一性原理模拟准确和无偏的结构推断的多个吸附。相应的AFM模拟,然后允许指纹吸附物结构中出现的AFM实验图像。在大体积的(1 S)-樟脑吸附在Cu(111)表面的情况下,三个匹配的AFM图像对比度被发现,这使得相关的实验图像特征的分子吸附的不同情况下。

英文摘要:

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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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