Exploring thermal expansion of carbon-based nanosheets by machine-learning interatomic potentials

authored by
B Mortazavi, A Rajabpour, XY Zhuang, T Rabczuk, AV Shapeev
Abstract

Examination of thermal expansion of two-dimensional (2D) nanomaterials is a challenging theoretical task with either ab-initio or classical molecular dynamics simulations. In this regard, while ab-initio molecular dynamics (AIMD) simulations offer extremely accurate predictions, but they are excessively demanding from computational point of view. On the other side, classical molecular dynamics simula-tions can be conducted with affordable computational costs, but without predictive accuracy needed to study novel materials and compositions. Herein, we explore the thermal expansion of several carbon -based nanosheets on the basis of machine-learning interatomic potentials (MLIPs). We show that passively trained MLIPs over inexpensive AIMD trajectories enable the examination of thermal expansion of complex nanomembranes over wide range of temperatures. Passively fitted MLIPs could also with outstanding accuracy reproduce the phonon dispersion relations predicted by density functional theory calculations. Our results highlight that the devised methodology on the basis of passively trained MLIPs is computationally efficient and versatile to accurately examine the thermal expansion of complex and novel materials and compositions using the molecular dynamics simulations. (c) 2021 Elsevier Ltd. All rights reserved.

Organisation(s)
PhoenixD: Photonics, Optics, and Engineering - Innovation Across Disciplines
Institute of Photonics
External Organisation(s)
Imam Khomeini International University
Tongji University
Skolkovo Innovation Center
Type
Article
Journal
CARBON
Volume
186
Pages
501-508
No. of pages
8
ISSN
0008-6223
Publication date
01.2022
Publication status
Published
Peer reviewed
Yes
ASJC Scopus subject areas
Chemistry(all), Materials Science(all)
Electronic version(s)
https://doi.org/10.1016/j.carbon.2021.10.059 (Access: Closed)