Vacancy defects impede the transition from peapods to diamond: a neuroevolution machine learning study

Area of Science:

• Materials Science
• Computational Materials Science
• Nanotechnology

Background:

• Carbon peapods are promising precursors for novel carbon allotropes.
• Accurate simulations are crucial for understanding their structural transitions.
• Existing simulation potentials may lack accuracy or be computationally expensive.

Purpose of the Study:

• To develop a high-accuracy, low-cost machine-learned potential (MLP) for carbon materials.
• To investigate structural transitions in carbon peapod arrays under high temperature and pressure.
• To understand the impact of vacancy defects on these transitions.

Main Methods:

• Utilized the neuroevolution potential framework to create the MLP.
• Conducted large-scale molecular dynamics simulations of peapod arrays.
• Analyzed structural changes and defect impacts under varying conditions.

Main Results:

• Developed a precise and cost-effective MLP for carbon materials.
• Identified that defects promote amorphous structures at low temperatures but hinder diamond formation.
• Reproduced experimentally observed carbon structures through simulations.

Conclusions:

• The developed MLP accurately simulates structural transitions in carbon peapods.
• Vacancy defects play a critical role in directing the formation of different carbon structures.
• This work provides a valuable tool for exploring new carbon allotropes.