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Updated: May 12, 2025

Probing C84-embedded Si Substrate Using Scanning Probe Microscopy and Molecular Dynamics
Published on: September 28, 2016
Unveiling fullerene formation and interconversion through molecular dynamics simulations with deep neural network
Yanbo Han1, Mengyang Li2, Masahiro Ehara3
1School of Chemistry, State Key Laboratory of Electrical Insulation and Power Equipment, Xi'an Jiaotong University, Xi'an 710049, China. xzhao@mail.xjtu.edu.cn.
Abstract:
Utilizing deep neural network potentials within molecular dynamics simulations, this research uncovers insights into fullerene formation and interconversion, particularly during the cooling stage of the annealing process. Our deep learning-enhanced approach effectively models the generation of fullerenes from C2 units in carbon vapor, highlighting the critical role of carbon density in structuring outcomes in a primary iron-carbon system. This study provides differences in molecular dynamics simulations for fullerene generation and also demonstrates the potential of deep learning in investigating complex carbon structures, paving the way for further investigations into the fullerene family.
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