Nicholas Humphrey, Selin Bac, Shaama Mallikarjun Sharada
{"title":"A configuration sampling study of reaction intermediates constituting catalytic cycles for CO oxidation with Pt1/TiO2.","authors":"Nicholas Humphrey, Selin Bac, Shaama Mallikarjun Sharada","doi":"10.1063/5.0225962","DOIUrl":null,"url":null,"abstract":"<p><p>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.</p>","PeriodicalId":15313,"journal":{"name":"Journal of Chemical Physics","volume":null,"pages":null},"PeriodicalIF":3.1000,"publicationDate":"2024-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Chemical Physics","FirstCategoryId":"92","ListUrlMain":"https://doi.org/10.1063/5.0225962","RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"CHEMISTRY, PHYSICAL","Score":null,"Total":0}
引用次数: 0
Abstract
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.
Topical coverage includes:
Theoretical Methods and Algorithms
Advanced Experimental Techniques
Atoms, Molecules, and Clusters
Liquids, Glasses, and Crystals
Surfaces, Interfaces, and Materials
Polymers and Soft Matter
Biological Molecules and Networks.