Unsupervised machine learning methods for polymer nanocomposites data via molecular dynamics simulation-Reference-Cited by-同舟云学术

Unsupervised machine learning methods for polymer nanocomposites data via molecular dynamics simulation

Published:2020-12-02 Issue:18 Volume:46 Page:1509-1521
ISSN:0892-7022
Container-title:Molecular Simulation
language:en
Short-container-title:Molecular Simulation

Author:

Chen Zhudan¹,Li Dazi¹,Wan Haixiao²,Liu Minghui²,Liu Jun²

Affiliation:

1. Institute of Automation, Beijing University of Chemical Technology, Beijing, People’s Republic of China

2. Key Laboratory of Beijing City on Preparation and Processing of Novel Polymer Materials, Beijing University of Chemical Technology, Beijing, People’s Republic of China

Funder

National Natural Science Foundation of China

Beijing Natural Science Foundation

Publisher

Informa UK Limited

Subject

Condensed Matter Physics,General Materials Science,General Chemical Engineering,Modeling and Simulation,Information Systems,General Chemistry

Link

https://www.tandfonline.com/doi/pdf/10.1080/08927022.2020.1851028

Reference61 articles.

1. Polymer nanocomposites

2. Polymer composite for antistatic application in aerospace

3. Dielectric Properties of Polymer–Particle Nanocomposites Influenced by Electronic Nature of Filler Surfaces

4. Graphene reinforced nanocomposites: 3D simulation of damage and fracture

5. Synthesis of Novel (Polymer Blend-Ceramics) Nanocomposites: Structural, Optical and Electrical Properties for Humidity Sensors

Cited by 8 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Widom Line in Supercritical Water in Terms of Changes in Local Structure: Theoretical Perspective;The Journal of Physical Chemistry Letters;2024-05-24

2. GATBoost: Mining graph attention networks-based important substructures of polymers for a better property prediction;Materials Today Communications;2024-03

3. A 30-Year Review on Nanocomposites: Comprehensive Bibliometric Insights into Microstructural, Electrical, and Mechanical Properties Assisted by Artificial Intelligence;Materials;2024-02-27

4. Data-driven analysis of dynamical heterogeneity in polymer melts near surfaces;Computational Materials Science;2024-02

5. Accelerating the design and development of polymeric materials via deep learning: Current status and future challenges;APL Machine Learning;2023-04-07