Application of graph neural network in computational heterogeneous catalysis.
Zihao Jiao1,2, Ya Liu1, Ziyun Wang2
1International Research Center for Renewable Energy, State Key Laboratory of Multiphase Flow in Power Engineering, Xi'an Jiaotong University, Shaanxi 710049, China.
Graph neural networks (GNNs) offer powerful tools for understanding complex heterogeneous catalysis reactions. These networks accelerate materials screening and potential energy surface exploration, advancing chemical industry innovations.
Area of Science:
- Chemistry
- Materials Science
- Computer Science
Background:
- Heterogeneous catalysis is crucial for chemical industries but faces theoretical challenges due to complex reaction processes.
- Graph neural networks (GNNs) are emerging as powerful tools due to their ability to learn atomic representations and relationships.
Purpose of the Study:
- To introduce the principles, architectures, and datasets of GNNs.
- To review GNN applications in heterogeneous catalysis, focusing on materials screening and potential energy surface exploration.
Main Methods:
- Introduction to basic GNN principles and network architectures.
- Review of GNN applications in accelerating materials screening.
- Exploration of GNNs for mapping potential energy surfaces in catalytic reactions.
Main Results:
- GNNs demonstrate significant potential in analyzing complex catalytic systems.
- Applications include efficient screening of catalytic materials.
- GNNs aid in understanding reaction mechanisms by exploring potential energy surfaces.
Conclusions:
- GNNs are a promising tool for advancing heterogeneous catalysis research.
- Challenges and future prospects of GNNs in this field are discussed.
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