Turchi Mattia, Galmarini Sandra, Lunati Ivan
Laboratory for Computational Engineering, Swiss Federal Laboratories for Materials Science and Technology, Empa, Überlandstrasse 129, 8600 Dübendorf, Switzerland.
Laboratory for Building Energy Materials and Components, Swiss Federal Laboratories for Materials Science and Technology, Empa, Überlandstrasse 129, 8600 Dübendorf, Switzerland.
J Chem Theory Comput. 2024 Sep 10;20(17):7597-7610. doi: 10.1021/acs.jctc.4c00702. Epub 2024 Aug 26.
The physicochemical heterogeneity found on amorphous surfaces leads to a complex interaction of adsorbate molecules with topological and undercoordinated defects, which enhance the adsorption capacity and can participate in catalytic reactions. The identification and analysis of the adsorption structure observed on amorphous surfaces require novel tools that allow the segmentation of the surfaces into complex-shaped regions that contrast with the periodic patterns found on crystalline surfaces. We propose a Random Forest (RF) classifier that segments the surface into regions that can then be further analyzed and classified to reveal the dynamics of the interaction with the adsorbate. The RF segmentation is applied to the surface density map of the adsorbed molecules and employs multiple features (intensity, gradient, and the eigenvalues of the Hessian matrix) which are nonlocal and allow a better identification of the adsorption structures. The segmentation depends on a set of parameters that specify the training set and can be tailored to serve the specific purpose of the segmentation. Here, we consider an example in which we aim to separate highly heterogeneous regions from weakly heterogeneous regions. We demonstrate that the RF segmentation is able to separate the surface into a fully connected weakly heterogeneous region (whose behavior is somehow similar to crystalline surfaces and has an exponential distribution of the residence time) and a very heterogeneous region characterized by a complex residence-time distribution, which is generated by the undercoordinated defects and is responsible for the peculiar characteristics of the amorphous surface.
在非晶态表面发现的物理化学不均匀性导致吸附质分子与拓扑和配位不足缺陷发生复杂相互作用，这增强了吸附能力并能参与催化反应。对非晶态表面观察到的吸附结构进行识别和分析需要新颖的工具，这些工具能将表面分割成形状复杂的区域，与晶体表面发现的周期性图案形成对比。我们提出一种随机森林（RF）分类器，它将表面分割成多个区域，然后可以对这些区域进行进一步分析和分类，以揭示与吸附质相互作用的动态过程。RF分割应用于吸附分子的表面密度图，并采用多种特征（强度、梯度和黑塞矩阵的特征值），这些特征是非局部的，能更好地识别吸附结构。分割取决于一组指定训练集的参数，并且可以根据分割的特定目的进行定制。在此，我们考虑一个例子，即我们旨在将高度不均匀区域与弱不均匀区域分开。我们证明，RF分割能够将表面分为一个完全连通的弱不均匀区域（其行为在某种程度上类似于晶体表面，且停留时间呈指数分布）和一个非常不均匀的区域，该区域具有复杂的停留时间分布，由配位不足缺陷产生，并且决定了非晶态表面的独特特性。
