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A data-driven leap towards stable catalysts
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DOI：10.1038/s41929-025-01428-0.png)
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Abstract
En 中文
Achieving long-term catalyst stability remains a grand challenge in catalysis. A recent study combines neural-network potential-based molecular dynamics simulations with decision tree-based interpretable machine learning, unveiling crucial support properties that guide the rational design of sinter-resistant platinum catalysts.
Keywords:
catalyst stability
neural-network potential
molecular dynamics
decision tree
platinum catalyst
Journal
IF:
44.6
Papers:
353
Citations:
3.2W