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Machine-learning atomic simulation for heterogeneous catalysis
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DOI：10.1038/s41524-022-00959-5.png)
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Abstract
En 中文
Heterogeneous catalysis is at the heart of chemistry. New theoretical methods based on machine learning (ML) techniques that emerged in recent years provide a new avenue to disclose the structures and reaction in complex catalytic systems. Here we review briefly the history of atomic simulations in catalysis and then focus on the recent trend shifting toward ML potential calculations. The advanced methods developed by our group are outlined to illustrate how complex structures and reaction networks can be resolved using the ML potential in combination with efficient global optimization methods. The future of atomic simulation in catalysis is outlooked.
Keywords:
SURFACE WALKING METHOD
DENSITY-FUNCTIONAL THEORY
STRUCTURE PREDICTION
GLOBAL OPTIMIZATION
TRANSITION-STATE
ETHYLENE EPOXIDATION
CRYSTAL-STRUCTURE
AMMONIA-SYNTHESIS
PHASE-TRANSITION
CO OXIDATION
Journal
IF:
11.9
Papers:
2.4K
Citations:
1.7W
Organization
Cited Papers
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CHEMICAL SCIENCE
OAAI
CHEMICAL SCIENCEIF7.4
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