Nature of Metal-Support Interaction Discovered by Interpretable Machine Learning
crossref（2024）
University of Science and Technology of China | Shanghai University
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 systems.
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