Learning Adsorption Patterns on Amorphous Surfaces,Journal of Chemical Theory and Computation

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Learning Adsorption Patterns on Amorphous Surfaces
Journal of Chemical Theory and Computation ( IF 5.7 ) Pub Date : 2024-08-26 , DOI: 10.1021/acs.jctc.4c00702
Mattia Turchi ₁ , Sandra Galmarini ₂ , Ivan Lunati ₁

Affiliation

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.

中文翻译：

学习非晶表面上的吸附模式

非晶表面上的物理化学异质性导致吸附物分子与拓扑和欠配位缺陷之间发生复杂的相互作用，从而增强吸附能力并参与催化反应。在非晶表面上观察到的吸附结构的识别和分析需要新颖的工具，可以将表面分割成复杂形状的区域，与晶体表面上发现的周期性图案形成鲜明对比。我们提出了一种随机森林（RF）分类器，将表面分割成多个区域，然后可以进一步分析和分类这些区域，以揭示与吸附物相互作用的动态。 RF 分割应用于吸附分子的表面密度图，并采用多个非局部特征（强度、梯度和 Hessian 矩阵的特征值），可以更好地识别吸附结构。分割取决于一组指定训练集的参数，并且可以进行定制以服务于分割的特定目的。在这里，我们考虑一个例子，其中我们的目标是将高度异质性区域与弱异质性区域分开。我们证明，RF 分割能够将表面分成完全连接的弱异质区域（其行为在某种程度上类似于晶体表面，并且具有停留时间的指数分布）和以复杂停留时间为特征的非常异质区域分布，它是由欠配位缺陷产生的，是非晶表面独特特征的原因。

更新日期：2024-08-26

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