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Extendable Machine Learning Model for the Stability of Single Atom Alloys
Topics in Catalysis ( IF 3.6 ) Pub Date : 2020-04-29 , DOI: 10.1007/s11244-020-01267-2
Karun K. Rao , Quan K. Do , Khoa Pham , Debtanu Maiti , Lars C. Grabow

In this work, we aim to update the understanding of how impurity or promoter metals segregate on metal surfaces, particularly in the application of single-atom alloys (SAA) for catalysis. Using density functional theory, we calculated the relative stability of the idealized SAA relative to subsurface, dimer, and adatom configurations to determine the tendency of the promoter atom to diffuse into the bulk, form surface clusters, or avoid alloying with the host, respectively. We selected 26 d-block metals augmented with Al and Pb to create a 28 × 28 database that indicates a total of 250 combinations for which the SAA configuration is most stable, and an additional 358 systems for which the SAA geometry is within 0.5 eV of the most stable configuration. We classified the data using decision tree, support vector machine, and neural network machine learning algorithms with tabulated atomic properties as the input vector. These black box approaches are unable to extrapolate to other possible geometries, which was circumvented by redefining the stability problem as a regression. We propose a physical bond counting model to formulate intuitive criteria for the formation of stable SAAs. The accuracy is then improved by using the bonding configuration and tabulated atomic properties with a kernel ridge regression (KRR) algorithm. The hybrid KRR model correctly identifies 190 SAAs with 85 false positives. Importantly, its physical basis allows the hybrid model to extend to similar geometries not included in the training data, thereby expanding the domain where the model is useful.



中文翻译:

单原子合金稳定性的可扩展机器学习模型

在这项工作中,我们旨在更新对杂质或助催化剂金属如何在金属表面偏析的理解,尤其是在单原子合金(SAA)催化应用中。使用密度泛函理论,我们计算了理想化SAA相对于亚表面,二聚体和吸附原子构型的相对稳定性,以确定促进剂原子扩散进入主体,形成表面簇或避免与主体形成合金的趋势。我们选择了26填充Al和Pb的块金属,以创建28×28数据库,该数据库指示SAA配置最稳定的250个组合,以及SAA几何形状最稳定配置在0.5 eV以内的358个系统。我们使用决策树,支持向量机和神经网络机器学习算法对数据进行分类,并将列表化的原子属性作为输入向量。这些黑匣子方法无法外推到其他可能的几何形状,这是通过将稳定性问题重新定义为回归来避免的。我们提出了一种物理债券计数模型,以为形成稳定的SAA制定直观的标准。然后,通过使用键配置和具有核脊线回归(KRR)算法的列表化原子特性,可以提高准确性。混合KRR模型可以正确识别190个SAA,其中包含85个误报。重要的是,它的物理基础使混合模型可以扩展到训练数据中不包括的相似几何形状,从而扩展了模型有用的领域。

更新日期:2020-04-29
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