The Application of AI and Machine Learning for Energy Material Design
A special issue of Crystals (ISSN 2073-4352). This special issue belongs to the section "Materials for Energy Applications".
Deadline for manuscript submissions: closed (20 August 2024) | Viewed by 621
Special Issue Editors
Interests: quantum mechanics; computer science; artificial intelligence; drug design; cheminformatics
Special Issue Information
Dear Colleagues,
The development of a high-performance methodology for functional energy material (EM) discovery has become increasingly important against the background of the global energy crisis. Recently, the occurrence of novel AI and Machine Learning technologies has largely facilitated material designs that have crystal structures; and the obtained computational insights could be further instructive for experimental work. To accelerate functional energy material (EM) discovery, various kinds of deep learning architectures have been utilized for crystal structure predictions and optimization, like Graph Convolutional Network (GCN), Convolution Neural Network (CNN), etc. The aim of this issue is to collect AI and Machine Learning-based computational papers focusing on energy material design.
Dr. Peng Gao
Dr. Liangchen Liu
Guest Editors
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Keywords
- machine learning
- deep learning
- artificial intelligence
- graph convolutional network
- first-principles calculations
- microstructure modeling
- cheminformatics
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