Machine Learning in Thermal Resistance and Synthesis of Graphene Composites
A special issue of Processes (ISSN 2227-9717). This special issue belongs to the section "Materials Processes".
Deadline for manuscript submissions: closed (29 September 2023) | Viewed by 259
Special Issue Editors
Interests: graphene; carbon nanotube; machine learning; reinforcement learning; NLP
Interests: physics; machine learning
Special Issue Information
Dear Colleagues,
Graphene and its derivatives have attracted significant attention due to their unique properties such as high thermal conductivity and mechanical strength. The integration of graphene into composites has the potential to enhance the thermal resistance and mechanical properties of the resulting materials. However, the synthesis of graphene composites and understanding their thermal resistance properties are still challenging tasks.
Machine learning techniques have shown to be powerful tools in the analysis and prediction of material properties. In this Special Issue, we aim to showcase the latest advances in the application of machine learning to the synthesis and characterization of graphene composites, with a focus on their thermal resistance properties.
Topics include, but are not limited to:
- Machine learning-based synthesis of graphene composites;
- Machine learning-based prediction of thermal resistance properties of graphene composites;
- Machine learning-based characterization of graphene composites;
- Machine learning-based optimization of synthesis conditions for graphene composites;
- Machine learning-based modeling of thermal transport in graphene composites.
We invite researchers to submit their latest findings in this exciting field, and hope you will consider participating in this Special Issue.
Dr. Mingguang Chen
Prof. Dr. Yas Al-Hadeethi
Dr. Zhihui Shao
Guest Editors
Manuscript Submission Information
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Keywords
- machine learning
- graphene composites
- thermal resistance
- synthesis
- characterization
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