Computational Chemistry and Machine Learning-Assisted Screening of Supported Amorphous Metal Oxide Nanoclusters for Methane Activation

Activating the C–H bond in methane represents a cornerstone challenge in catalytic research. While several supported metal oxide nanoclusters (MeO-NCs) have shown promise for this reaction, their optimal composition remains underexplored primarily due to the large number of possible compositions and...

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Bibliographic Details
Main Author:	Xijun Wang (413258) (author)
Other Authors:	Kaihang Shi (1558654) (author), Anyang Peng (5039447) (author), Randall Q. Snurr (767831) (author)
Published:	2024
Subjects:	Biophysics Biochemistry Sociology Biological Sciences not elsewhere classified Chemical Sciences not elsewhere classified Information Systems not elsewhere classified row transition metals results offer insights process allowed us material design space less effective ones free energy barriers findings help narrow clustering model capable analysis uncovered correlations friendly methane conversion copper oxide nanocluster 12 new compositions methane activation despite methane activation activating methane activation methane represents possible compositions base nanocluster zn ). systematically substituted study addresses structural properties structural flexibility strong tolerance shown promise machine learning large number intrinsic electronic electronic structures distinguishing high cu sites cornerstone challenge consuming high computational chemistry challenges posed catalytic research catalytic activity assisted screening amorphous nature
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Computational Chemistry and Machine Learning-Assisted Screening of Supported Amorphous Metal Oxide Nanoclusters for Methane Activation

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