催化材料描述的超越标度关系
Beyond scaling relations for the description of catalytic materials
摘要：
计算筛选新的和改进的催化剂材料依赖于准确和低成本的关键参数，如吸附能的预测。在这里，我们使用最近开发的压缩传感方法来识别其预测能力扩展到广泛的吸附物，多金属过渡金属表面和小平面的描述符。描述符被表示为清洁催化剂表面的固有性质的非线性函数，例如，配位数，d带矩和费米能级的态密度。从一个单一的密度泛函理论计算这些属性，我们预测吸附能在所有潜在的表面网站，从而也是最稳定的几何形状。与以前的方法（如标度关系）相比，我们发现我们的方法对于预测具有混合金属表面的合金上的吸附能更一般，更准确，当基于仅包括纯金属的训练数据时。这种准确性可以通过将合金吸附能添加到训练数据中来系统地提高。
英文摘要：
Computational screening for new and improved catalyst materials relies on accurate and low-cost predictions of key parameters such as adsorption energies. Here, we use recently developed compressed sensing methods to identify descriptors whose predictive power extends over a wide range of adsorbates, multi-metallic transition metal surfaces and facets. The descriptors are expressed as non-linear functions of intrinsic properties of the clean catalyst surface, e.g. coordination numbers, d-band moments and density of states at the Fermi level. From a single density-functional theory calculation of these properties, we predict adsorption energies at all potential surface sites, and thereby also the most stable geometry. Compared to previous approaches such as scaling relations, we find our approach to be both more general and more accurate for the prediction of adsorption energies on alloys with mixed-metal surfaces, already when based on training data including only pure metals. This accuracy can be systematically improved by adding also alloy adsorption energies to the training data.
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