Computational Nanotechnology: From Clusters to Devices

2005 ◽  
Author(s):  
David Tománek
Keyword(s):  
Computational Nanotechnology

Computational nanotechnology with carbon nanotubes and fullerenes

2001 ◽  
Vol 3 (4) ◽  
pp. 42-55 ◽  
Author(s):  
D. Srivastava ◽  
M. Menon ◽  
Kyeongjae Cho
Keyword(s):  
Carbon Nanotubes ◽  
Computational Nanotechnology

Applications of Machine learning in Computational Nanotechnology

2021 ◽  
Author(s):  
Wenxiang Liu ◽  
Yongqiang Wu ◽  
Yang Hong ◽  
Zhongtao Zhang ◽  
Yanan Yue ◽  
...  
Keyword(s):  
Machine Learning ◽  
Density Functional ◽  
State Of The Art ◽  
Material Design ◽  
Research Progress ◽  
Time Investment ◽  
Research Activities ◽  
Art Research ◽  
Applications Of Machine Learning ◽  
Computational Nanotechnology

Abstract Machine learning (ML) has gained extensive attentions in recent years due to its powerful data analysis capabilities. It has been successfully applied to many fields and helped the researchers to achieve several major theoretical and applied breakthroughs. Some of the notable applications in the field of computational nanotechnology are machine learning potentials, property prediction and material discovery. This review summarizes of the state-of-the-art research progress in these three fields. Machine learning potentials bridge the efficiency vs. accuracy gap between density functional calculations (DFT) and classical molecular dynamics (MD). For property predictions, machine learning provides a robust method that eliminate the needs of repetitive calculations for different simulation setup. Material design and drug discovery assisted by machine learning greatly reduces the capital and time investment by orders of magnitude. In this perspective, several common machine learning potentials and machine learning models are firstly introduced. Using these state-of-the-art models, developments in property predictions and material discovery are overviewed, respectively. Finally, this paper was concluded with an outlook on future directions of data-driven research activities in computational nanotechnology.

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