With the development of artificial intelligence (AI), AI plus science is increasingly valued, presenting new perspectives to scientific research. The research on using machine learning (including deep learning) to discover patterns from data and predict targeted material properties has received widespread attention, which will have a profound impact in material science studies. In recent years, there has been an increased interest in the use of deep learning in materials science, which has led to significant progress in both fundamental and applied research. One of the most notable advancements is the development of graph convolutional neural network models, which combine graph neural networks and convolutional neural networks to achieve outstanding results in materials science and bridge effectively the deep learning models and material properties predictions. The availability of large materials databases due to the rise of big data has further enhanced the relevance of these models in the field. We present, in this article, a comprehensive overview of graph convolutional neural network models, explaining their fundamental principles and highlighting a few examples of their applications in materials science, as well as current trends. The limitations and challenges that these models face, as well as the potential for future research in this dynamic area are also discussed.
| Published in | Advances in Applied Sciences (Volume 9, Issue 2) |
| DOI | 10.11648/j.aas.20240902.11 |
| Page(s) | 17-30 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2024. Published by Science Publishing Group |
Materials Science, Deep Learning, Graph Convolutional Neural Network
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APA Style
Zhao, K., Li, Q. (2024). Recent Advances and Applications of Graph Convolution Neural Network Methods in Materials Science. Advances in Applied Sciences, 9(2), 17-30. https://doi.org/10.11648/j.aas.20240902.11
ACS Style
Zhao, K.; Li, Q. Recent Advances and Applications of Graph Convolution Neural Network Methods in Materials Science. Adv. Appl. Sci. 2024, 9(2), 17-30. doi: 10.11648/j.aas.20240902.11
AMA Style
Zhao K, Li Q. Recent Advances and Applications of Graph Convolution Neural Network Methods in Materials Science. Adv Appl Sci. 2024;9(2):17-30. doi: 10.11648/j.aas.20240902.11
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Download@article{10.11648/j.aas.20240902.11,
author = {Ke-Lin Zhao and Qing-Xu Li},
title = {Recent Advances and Applications of Graph Convolution Neural Network Methods in Materials Science
},
journal = {Advances in Applied Sciences},
volume = {9},
number = {2},
pages = {17-30},
doi = {10.11648/j.aas.20240902.11},
url = {https://doi.org/10.11648/j.aas.20240902.11},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.aas.20240902.11},
abstract = {With the development of artificial intelligence (AI), AI plus science is increasingly valued, presenting new perspectives to scientific research. The research on using machine learning (including deep learning) to discover patterns from data and predict targeted material properties has received widespread attention, which will have a profound impact in material science studies. In recent years, there has been an increased interest in the use of deep learning in materials science, which has led to significant progress in both fundamental and applied research. One of the most notable advancements is the development of graph convolutional neural network models, which combine graph neural networks and convolutional neural networks to achieve outstanding results in materials science and bridge effectively the deep learning models and material properties predictions. The availability of large materials databases due to the rise of big data has further enhanced the relevance of these models in the field. We present, in this article, a comprehensive overview of graph convolutional neural network models, explaining their fundamental principles and highlighting a few examples of their applications in materials science, as well as current trends. The limitations and challenges that these models face, as well as the potential for future research in this dynamic area are also discussed.
},
year = {2024}
}
TY - JOUR T1 - Recent Advances and Applications of Graph Convolution Neural Network Methods in Materials Science AU - Ke-Lin Zhao AU - Qing-Xu Li Y1 - 2024/04/29 PY - 2024 N1 - https://doi.org/10.11648/j.aas.20240902.11 DO - 10.11648/j.aas.20240902.11 T2 - Advances in Applied Sciences JF - Advances in Applied Sciences JO - Advances in Applied Sciences SP - 17 EP - 30 PB - Science Publishing Group SN - 2575-1514 UR - https://doi.org/10.11648/j.aas.20240902.11 AB - With the development of artificial intelligence (AI), AI plus science is increasingly valued, presenting new perspectives to scientific research. The research on using machine learning (including deep learning) to discover patterns from data and predict targeted material properties has received widespread attention, which will have a profound impact in material science studies. In recent years, there has been an increased interest in the use of deep learning in materials science, which has led to significant progress in both fundamental and applied research. One of the most notable advancements is the development of graph convolutional neural network models, which combine graph neural networks and convolutional neural networks to achieve outstanding results in materials science and bridge effectively the deep learning models and material properties predictions. The availability of large materials databases due to the rise of big data has further enhanced the relevance of these models in the field. We present, in this article, a comprehensive overview of graph convolutional neural network models, explaining their fundamental principles and highlighting a few examples of their applications in materials science, as well as current trends. The limitations and challenges that these models face, as well as the potential for future research in this dynamic area are also discussed. VL - 9 IS - 2 ER -
School of Science, Chongqing University of Posts and Telecommunications, Chongqing, China; Institute for Advanced Sciences, Chongqing University of Posts and Telecommunications, Chongqing, China
