Thermophysical properties of adsorbates with beyond-DFT accuracy from DFT data through error cancellation.

IF 3.1 3区 化学 Q2 CHEMISTRY, PHYSICAL
Seth G Porter, Bjarne Kreitz
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引用次数: 0

Abstract

Predictive multiscale modeling of heterogeneously catalyzed reactions requires accurate enthalpies of adsorbates. These properties are typically calculated from density functional theory (DFT) using exchange-correlation functionals with the generalized-gradient approximation (GGA) since more accurate electronic structure methods are not feasible. Therefore, the derived enthalpies are subject to large inaccuracies. We address this challenge through an error-cancellation approach that builds on the connectivity-based hierarchy (CBH) to derive enthalpies of formation of adsorbates with beyond-DFT accuracy without increasing computational cost. This method constructs reactions that conserve the electronic configuration between the target and the reference species, leading to error cancellation. The method is applied to adsorbates on Pt(111), Ni(111), and MgO(100). With the CBH method, it is possible to determine enthalpies of formation that are in excellent agreement with experimental measurements for a range of adsorbates and across many GGA exchange-correlation functionals, clearly outperforming conventional referencing approaches. Additionally, the method combines available experimental surface science data with gas-phase thermochemistry data and DFT data in a global thermochemical network. More accurate enthalpies of formation have a tremendous impact on the predictive performance of multiscale models and enable more conclusive insights into reaction mechanisms of catalytic reactions.

通过误差抵消,从DFT数据得到了具有超DFT精度的吸附剂的热物理性质。
多相催化反应的多尺度预测模型需要准确的吸附焓。这些性质通常是通过密度泛函理论(DFT)使用交换相关泛函与广义梯度近似(GGA)来计算的,因为更精确的电子结构方法是不可实现的。因此,推导出的焓有很大的不准确性。我们通过建立在基于连接的层次结构(CBH)上的误差消除方法来解决这一挑战,该方法可以在不增加计算成本的情况下以超过dft的精度推导吸附物的形成焓。该方法构建的反应保留了目标和参考物质之间的电子构型,从而消除了误差。该方法应用于Pt(111)、Ni(111)和MgO(100)的吸附。使用CBH方法,可以确定与一系列吸附物和许多GGA交换相关功能的实验测量结果非常一致的生成焓,明显优于传统的参考方法。此外,该方法将现有的实验表面科学数据与全球热化学网络中的气相热化学数据和DFT数据相结合。更精确的生成焓对多尺度模型的预测性能有巨大的影响,并使我们对催化反应的反应机理有更确切的认识。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Faraday Discussions
Faraday Discussions 化学-物理化学
自引率
0.00%
发文量
259
期刊介绍: Discussion summary and research papers from discussion meetings that focus on rapidly developing areas of physical chemistry and its interfaces
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