Machine Learning-Based Prediction of Mechanical Properties and Performance of Nickel–Graphene Nanocomposites Using Molecular Dynamics Simulation Data
Author:
Affiliation:
1. Department of Mechanical Engineering, Tsinghua University, Beijing 100084, P. R. China
2. State Key Laboratory of Tribology, Tsinghua University, Beijing 100084, P. R. China
Funder
Natural Science Foundation of Beijing Municipality
National Natural Science Foundation of China
Publisher
American Chemical Society (ACS)
Subject
General Materials Science
Link
https://pubs.acs.org/doi/pdf/10.1021/acsanm.3c01919
Reference47 articles.
1. Graphene-based composite materials
2. An overview of graphene and its derivatives reinforced metal matrix composites: Preparation, properties and applications
3. Enhanced corrosion resistance in metal matrix composites assembled from graphene encapsulated copper nanoflakes
4. Graphene–nickel composites
5. The effect of graphene content and sliding speed on the wear mechanism of nickel–graphene nanocomposites
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