Nicholas Humphrey, Selin Bac, Shaama Mallikarjun Sharada
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引用次数: 0
摘要
我们将自证分子动力学(AIMD)模拟与无监督机器学习方法相结合,自动搜索原子分散的 Pt1/TiO2 催化剂催化的一氧化碳氧化反应中间产物的可能构型。效仿 Roncoroni 及其合作者的研究[Phys. Chem. Chem. Phys. 25, 13741 (2023)],我们采用了 t 分布随机邻域嵌入和基于分层密度的空间聚类应用噪声来降低维度,并根据铂的局部配位环境对 AIMD 快照进行聚类。我们发现了新的局部极小值,尤其是在二氧化碳与活性位点结合的情况下,因为二氧化碳可以以各种方式与金属和支撑物进行配位。这些新的最小值构成了一些拟议的 CO 氧化途径中的额外基本步骤,导致周转频率与之前的估计值相差几个数量级。因此,这项研究表明,构型取样是原子分散催化剂催化循环计算研究的必要组成部分。
A configuration sampling study of reaction intermediates constituting catalytic cycles for CO oxidation with Pt1/TiO2.
We combine ab initio molecular dynamics (AIMD) simulations with an unsupervised machine learning approach to automate the search for possible configurations of CO oxidation reaction intermediates catalyzed by the atomically dispersed Pt1/TiO2 catalyst. Following the example of Roncoroni and co-workers [Phys. Chem. Chem. Phys. 25, 13741 (2023)], we employ t-distributed stochastic neighbor embedding and hierarchical density-based spatial clustering of applications with noise to reduce the dimensionality and cluster AIMD snapshots based on the local coordination environment of Pt. We identify new local minima, particularly in cases where CO2 is bound to the active site, because it can coordinate in various ways with both the metal and support. The new minima constitute additional elementary steps in some proposed pathways for CO oxidation, resulting in turnover frequencies that differ from prior estimates by several orders of magnitude. This work, therefore, demonstrates that configuration sampling is a necessary component of computational studies of catalytic cycles for atomically dispersed catalysts.
期刊介绍:
The Journal of Chemical Physics publishes quantitative and rigorous science of long-lasting value in methods and applications of chemical physics. The Journal also publishes brief Communications of significant new findings, Perspectives on the latest advances in the field, and Special Topic issues. The Journal focuses on innovative research in experimental and theoretical areas of chemical physics, including spectroscopy, dynamics, kinetics, statistical mechanics, and quantum mechanics. In addition, topical areas such as polymers, soft matter, materials, surfaces/interfaces, and systems of biological relevance are of increasing importance.
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