The development of carbon nanotube technology has provided a great advantage in applications of many fields including nanotechnology and materials science due to the exquisite mechanical, chemical, thermal and electrical properties of carbon nanotubes. However, due to their size, the scale at which the physical phenomena of carbon nanotubes are apparent is too small to do physical experiments, there is a need for certain computational methods like molecular dynamics simulations. In this present study, we propose a deep learning methodology, alongside a custom data preprocessing method, for precisely determining carbon nanotubes’ coordinates. We experimented with various topologies of neural networks and acquired a top result of 81.34%. Our findings and computation method surpasses the previous work on this field, in terms accuracy and computational time.
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Carbon Nanotube Coordinate Prediction with Deep Learning
Semantic Scholar · Materials Science · 2019
Abstract
The development of carbon nanotube technology has provided a great advantage in applications of many fields including nanotechnology and materials science due to the exquisite mechanical, chemical, thermal and electrical properties of carbon nanotubes. However, due to their size, the scale at which the physical phenomena of carbon nanotubes are apparent is too small to do physical experiments, there is a need for certain computational methods like molecular dynamics simulations. In this present study, we propose a deep learning methodology, alongside a custom data preprocessing method, for precisely determining carbon nanotubes’ coordinates. We experimented with various topologies of neural networks and acquired a top result of 81.34%. Our findings and computation method surpasses the previous work on this field, in terms accuracy and computational time.