Nature of ‎Metal-Support Interaction Discovered by Interpretable Machine ‎Learning

08 April 2024, Version 1
This content is a preprint and has not undergone peer review at the time of posting.

Abstract

Metal catalysts supported on oxides play a paramount role in numerous industrial reactions. ‎Modulating metal-support interaction is a key strategy to boost ‎catalytic productivity and ‎stability; however, the nature of metal-support interaction and quantification remain major ‎unsolved problems. By ‎leveraging interpretable ‎machine learning, domain knowledge, and ‎experimental data available, we discover a physical metal-support interaction equation ‎applicable ‎to metal nanoparticles and adatoms on oxides, and oxide films on metals. Though ‎metal-oxygen interaction dominates metal-support interaction and determines the metal ‎composition effect, metal-metal interaction delineates the support effect. This ensures a principle ‎of strong metal-metal interaction for encapsulation of suboxide over metal ‎nanoparticles, ‎substantiated comprehensively by molecular dynamics simulations and ‎previous experiments. The ‎developed theory provides valuable insights and guidance in engineering the metal-support ‎interface. ‎

Keywords

interpretability
encapsulation
metal-oxygen interaction
metal-metal interaction
metal-support interaction equation

Supplementary materials

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Description
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Supporting Information
Description
Materials and Methods; Supplementary Text; Figs. S1 to S22 ; Tables S1 to S7; References (51–131)
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