This document presents research on using graph neural networks and density functional theory calculations to discover graphene-based dual-atom catalysts for hydrogen evolution reaction. It introduces the computational methods used, including density functional theory and a graph convolutional neural network model. The results section discusses dataset construction, regression with the neural network model, high-throughput screening of candidates, and further density functional theory studies of proposed catalyst configurations.

1. School of Material Science and
Engineering
Northwestern Polytechnical
University
Presented to:
Professor Fuyi CHEN
Presented by:

2. Data-Driven Discovery of
Graphene-Based Dual-Atom
Catalysts for Hydrogen
Evolution Reaction with Graph
Neural Network and DFT
Calculations

3. ABSTRACT
1. INTRODUCTION
2. COMPUTATIONAL DETAILS
2.1 Density Functional Theory
2.2 Descriptors and Machine Learning Model.
3. RESULTS AND DISCUSSION
3.1 Dataset Construction.
3.2 Regression Using CGCNN
3.3 DFT/ML High-Throughput Screening.
3.4 DFT Studies of the Proposed Candidates.
4. CONCLUSIONS