论文标题
贝叶斯对原子分辨成像数据的ADATOM相互作用的学习
Bayesian learning of adatom interactions from atomically-resolved imaging data
论文作者
论文摘要
表面的原子结构和Adatom几何形状编码有关导致其形成的过程的热力学和动力学的信息，并且可以通过生成物理模型捕获。在这里，我们基于基于机器学习的扫描隧道显微镜图像的分析来开发工作流程，以重建原子和ADATOM位置，以及一个贝叶斯优化程序，以最大程度地减少所选物理模型和实验性观察之间的统计距离。我们优化了描述表面排序的2和3参数ISING模型的参数，并使用派生的生成模型在参数空间上进行预测。对于浓度依赖性，我们将不同ADATOM浓度下的预测形态与不同的区域的不同区域进行了比较，该样品表面偶然地具有不同的ADATOM浓度。提出的工作流程是通用的，可用于重建来自材料微结构的实验观察结果的热力学模型和相关的不确定性。手稿中使用的代码可在https://github.com/saimani5/adatom_interactions上获得。
Atomic structures and adatom geometries of surfaces encode information about the thermodynamics and kinetics of the processes that lead to their formation, and which can be captured by a generative physical model. Here we develop a workflow based on a machine learning-based analysis of scanning tunneling microscopy images to reconstruct the atomic and adatom positions, and a Bayesian optimization procedure to minimize statistical distance between the chosen physical models and experimental observations. We optimize the parameters of a 2- and 3-parameter Ising model describing surface ordering and use the derived generative model to make predictions across the parameter space. For concentration dependence, we compare the predicted morphologies at different adatom concentrations with the dissimilar regions on the sample surfaces that serendipitously had different adatom concentrations. The proposed workflow is universal and can be used to reconstruct the thermodynamic models and associated uncertainties from the experimental observations of materials microstructures. The code used in the manuscript is available at https://github.com/saimani5/Adatom_interactions.
