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

• Published in: Physical chemistry chemical physics : PCCP Vol. 25; no. 37; pp. 25629 - 25638
• Main Authors: Li, Yu, Jiang, Jin-Wu
• Format: Journal Article
• Language: English
• Published: Cambridge Royal Society of Chemistry 27.09.2023
• Subjects: Allotropy; Arrays; Carbon; Crystal defects; Diamond machining; Diamonds; High temperature; Low cost; Low temperature; Machine learning; Materials science; Molecular dynamics; Simulation
• ISSN: 1463-9076; 1463-9084; 1463-9084
• DOI: 10.1039/d3cp03862a

Exploration of novel carbon allotropes has been a central subject in materials science, in which carbon peapods hold great potential as a precursor for the development of new carbon allotropes. To enable precise large-scale molecular dynamics simulations, we develop a high-accurate and low-cost machine-learned potential (MLP) for carbon materials using the neuroevolution potential framework. Based on the MLP, we conduct an investigation into the structural transitions of peapod arrays under high-temperature and high-pressure conditions and disclose the impact of vacancy defects. Defects promote the transition from the ordered crystalline structure to the disordered amorphous structure in peapods at low temperatures, while impeding the transition to the ordered diamond structure. Benefiting from the accurate MLP, we are able to reproduce the experimentally observed carbon structures in numerical simulations. We build a diagram summarizing all the structures that appear in the compression simulation of peapod arrays at various temperatures. The present work not only discloses the underlying mechanism of structural transitions from carbon peapods into various functional carbon materials, but also provides a high-accurate and low-cost interatomic potential that shall be valuable in the exploration of novel carbon allotropes. The effect of vacancy defects on structural transitions in carbon peapods is investigated via developed machine-learned potential based on the neuroevolution potential framework.