期刊
CARBON
卷 202, 期 -, 页码 554-560出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.carbon.2022.11.007
关键词
Fullerene; Evolution diagram; Machine learning force field; Functional carbon materials
This study used a highly-accurate machine learning force field to investigate phase transformations in carbon structures and obtained a theoretical diagram that provides guidance for experimental synthesis of various functional carbon materials.
Although the transformation from fullerene bulk into various functional carbon materials at high temperature and high pressure (HTHP) has been extensively explored experimentally, the understanding of the mechanism of structural transformations at atomic scale is still very poor. Based on the recently developed highly-accurate machine learning force field (MLFF), GAP-20, we performed a systematic study on the phase transformations of carbon structures. Various derived structures, such as C60 foams, sp2, sp3, and mixed sp2-sp3 amorphous carbon materials are formed at temperatures below 2500 K, while graphitic carbon, nano-graphitic carbon and diamond are formed at higher temperatures. These materials exhibit excellent mechanical properties and can be used for various applications. The activation energy of the transformation from amorphous carbon to diamond is found to be 2.42 eV, which explains the high stability of sp3 amorphous carbon materials observed recently [Nature, 2021, 599:599; Nature, 2021, 599:605]. The theoretical diagram obtained in this study provides a guidance for experimental synthesis of various functional carbon materials.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据