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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.

Physical Chemistry Chemical Physics : PCCP

|April 23, 2025

Summary

Deep neural networks model fullerene formation from carbon vapor during annealing. This approach highlights carbon density's role in structuring outcomes, advancing fullerene research.

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Published on: October 12, 2019

Area of Science:

Computational materials science
Chemical physics
Nanotechnology

Background:

Fullerene formation mechanisms are complex and not fully understood, especially during material processing like annealing.
Traditional simulation methods face challenges in accurately modeling the intricate dynamics of carbon vapor interactions.

Purpose of the Study:

To investigate fullerene formation and interconversion during the cooling phase of annealing using advanced computational techniques.
To model the generation of fullerenes from C2 units in carbon vapor.
To explore the influence of carbon density on fullerene structures in an iron-carbon system.

Main Methods:

Employing deep neural network potentials within molecular dynamics simulations.
Utilizing a deep learning-enhanced approach to model carbon vapor behavior.
Performing simulations on a primary iron-carbon system.

Main Results:

Successfully modeled fullerene generation from C2 units in carbon vapor.
Identified the critical role of carbon density in determining structural outcomes.
Provided comparative insights into molecular dynamics simulations for fullerene generation.

Conclusions:

Deep learning significantly enhances the modeling of fullerene formation processes.
Carbon density is a key factor influencing fullerene structures in specific systems.
This research opens avenues for deeper exploration of the fullerene family using AI-driven methods.