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Structure Sensitivity of Metal Catalysts Revealed by Interp..:

Wu Shu ; Jiancong Li ; Jin-Xun Liu...
https://figshare.com/articles/dataset/Structure_Sensitivity_of_Metal_Catalysts_Revealed_by_Interpretable_Machine_Learning_and_First-Principles_Calculations/25408356. , 2024

Link: https://doi.org/10.1021/jacs.4c01524.s003

RT Journal
T1 <Title>Structure Sensitivity of Metal Catalysts Revealed by Interpretable Machine Learning and First-Principles Calculations</Title>

UL https://suche.suub.uni-bremen.de/peid=base-ftsmithonianinsp:oai:figshare.com:article_25408356&Exemplar=1&LAN=DE
				
A1 Wu Shu
A1 Jiancong Li
A1 Jin-Xun Liu
A1 Chuwei Zhu
A1 Tairan Wang
A1 Li Feng
A1 Runhai Ouyang
A1 Wei-Xue Li
YR 2024
K1 Biophysics
K1 Biochemistry
K1 Genetics
K1 Evolutionary Biology
K1 Astronomical and Space Sciences not elsewhere classified
K1 Biological Sciences not elsewhere classified
K1 Chemical Sciences not elsewhere classified
K1 linear scaling relation
K1 interpretable machine learning
K1 diverse chemical bonds
K1 almost one century
K1 17 different facets
K1 10 transition metals
K1 specific metal catalysts
K1 coordinated number mediated
K1 metal catalysts revealed
K1 principles data set
K1 metal catalysts
K1 data set
K1 principles calculations
K1 insights revealed
K1 supported catalysts
K1 efficient catalysts
K1 valence electrons
K1 successfully address
K1 structure term
K1 structure sensitivity
K1 structure descriptor
K1 steric hindrance
K1 reaction barriers
K1 rational design
K1 lattice constant
K1 heterogeneous catalysis
K1 explicit geometry
K1 entangled geometric
K1 electronic effects
K1 constructive strategy
K1 composition properties
K1 breaking barriers
K1 bond order
K1 active sites
JF https://figshare.com/articles/dataset/Structure_Sensitivity_of_Metal_Catalysts_Revealed_by_Interpretable_Machine_Learning_and_First-Principles_Calculations/25408356
LK http://dx.doi.org/https://doi.org/10.1021/jacs.4c01524.s003
DO https://doi.org/10.1021/jacs.4c01524.s003
SF ELIB - SuUB Bremen

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