{"title":"Machine Learning Accelerating Structure Prediction of PtSnO Nanoclusters under Working Conditions","authors":"Fanke Zeng, Wanglai Cen","doi":"10.1039/d4cp03769c","DOIUrl":null,"url":null,"abstract":"Credible properties exploring or prediction can not be achieved without well-established compositions and structures of catalysts under working conditions. We construct surrogate models via combination of machine learning (ML), genetic algorithms (GA) and ab initio thermodynamics (AITD) to accelerate global optimization of PtSn binary metals oxides, which is typically used for CO2-assisted propane dehydrogenation to propylene. This challenging case illustrates that the subtle oxidized states of PtSnO clusters can be predicted in a large chemical space including a wide range of reaction conditions. The oxidation patterns, phase diagrams and atomic charge distributions of the PtSnO clusters have been discussed. The Sn decorating mechanism to Pt in PtSnO has been explained. These results also indicate the oxidation of PtSn clusters are more feasible under working conditions, and that previous understanding obtained only with fully reduced PtSn alloy may be incomplete.","PeriodicalId":99,"journal":{"name":"Physical Chemistry Chemical Physics","volume":null,"pages":null},"PeriodicalIF":2.9000,"publicationDate":"2024-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Physical Chemistry Chemical Physics","FirstCategoryId":"92","ListUrlMain":"https://doi.org/10.1039/d4cp03769c","RegionNum":3,"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
Credible properties exploring or prediction can not be achieved without well-established compositions and structures of catalysts under working conditions. We construct surrogate models via combination of machine learning (ML), genetic algorithms (GA) and ab initio thermodynamics (AITD) to accelerate global optimization of PtSn binary metals oxides, which is typically used for CO2-assisted propane dehydrogenation to propylene. This challenging case illustrates that the subtle oxidized states of PtSnO clusters can be predicted in a large chemical space including a wide range of reaction conditions. The oxidation patterns, phase diagrams and atomic charge distributions of the PtSnO clusters have been discussed. The Sn decorating mechanism to Pt in PtSnO has been explained. These results also indicate the oxidation of PtSn clusters are more feasible under working conditions, and that previous understanding obtained only with fully reduced PtSn alloy may be incomplete.
期刊介绍:
Physical Chemistry Chemical Physics (PCCP) is an international journal co-owned by 19 physical chemistry and physics societies from around the world. This journal publishes original, cutting-edge research in physical chemistry, chemical physics and biophysical chemistry. To be suitable for publication in PCCP, articles must include significant innovation and/or insight into physical chemistry; this is the most important criterion that reviewers and Editors will judge against when evaluating submissions.
The journal has a broad scope and welcomes contributions spanning experiment, theory, computation and data science. Topical coverage includes spectroscopy, dynamics, kinetics, statistical mechanics, thermodynamics, electrochemistry, catalysis, surface science, quantum mechanics, quantum computing and machine learning. Interdisciplinary research areas such as polymers and soft matter, materials, nanoscience, energy, surfaces/interfaces, and biophysical chemistry are welcomed if they demonstrate significant innovation and/or insight into physical chemistry. Joined experimental/theoretical studies are particularly appreciated when complementary and based on up-to-date approaches.