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Machine Learning Interatomic Potential-Enabled Discovery of Chlorofullerenes.

Zi-Yang Qiu1,2, Wei-Wei Wang1, Qi Yang3

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Summary

This study introduces a machine learning potential for predicting chlorinated fullerene structures, enabling efficient screening and offering insights into their formation. This method accurately models experimentally known chlorofullerenes, aiding in understanding their stability and structural features.

Keywords:
MLPchlorinationchlorofullerenesdensity functional theoryfullerenes

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Area of Science:

  • Computational Chemistry
  • Materials Science
  • Nanotechnology

Background:

  • Chlorination stabilizes fullerenes by altering carbon hybridization and relieving strain.
  • Predicting structures of chlorinated fullerenes is challenging due to isomer diversity and varied chlorination sites.

Purpose of the Study:

  • To develop a machine learning potential (MLP) for the carbon-chlorine (C-Cl) system with density functional theory (DFT) accuracy.
  • To enable high-throughput screening of chlorinated fullerene structures.
  • To establish correlations between parent fullerenes and their chlorinated derivatives.

Main Methods:

  • Development of a DFT-accurate machine learning potential for the C-Cl system.
  • Application of the MLP for high-throughput screening of chlorinated fullerenes.
  • Analysis of structural features and stability of chlorinated fullerene derivatives.

Main Results:

  • The developed MLP accurately reproduces experimentally identified chlorofullerenes.
  • The study establishes correlations between parent fullerenes and their chlorinated forms.
  • Theoretical insights into the formation principles of chlorofullerenes are provided.

Conclusions:

  • The DFT-accurate MLP is a powerful tool for predicting chlorinated fullerene structures.
  • The findings facilitate understanding the structure-stability relationships in chlorofullerenes.
  • This work offers theoretical guidance for the synthesis and application of functionalized fullerenes.