TY - JOUR

T1 - Exploring thermal expansion of carbon-based nanosheets by machine-learning interatomic potentials

AU - Mortazavi, B

AU - Rajabpour, A

AU - Zhuang, XY

AU - Rabczuk, T

AU - Shapeev, AV

N1 - Funding Information: B.M. and X.Z. appreciate the funding by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation ) under Germany's Excellence Strategy within the Cluster of Excellence PhoenixD (EXC 2122, Project ID 390833453 ). A.V.S. was supported by the Russian Science Foundation (Grant No 18-13-00479 , https://rscf.ru/project/18-13-00479/ ). B. M. is also especially thankful to the VEGAS cluster at Bauhaus University of Weimar and Dr. Chernenko for the support of this study. A. R. appreciates Imam Khomeini International University Research Council for support of this study. Authors also acknowledge the support of the cluster team at the Leibniz Universität of Hannover .

PY - 2022/1

Y1 - 2022/1

N2 - 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.

AB - 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.

KW - Thermal expansion

KW - Graphene

KW - 2D materials

KW - Machine learning

KW - TOTAL-ENERGY CALCULATIONS

KW - GRAPHENE

KW - COEFFICIENT

KW - CONDUCTIVITY

KW - EFFICIENT

KW - C3N

UR - http://www.scopus.com/inward/record.url?scp=85117700988&partnerID=8YFLogxK

U2 - 10.1016/j.carbon.2021.10.059

DO - 10.1016/j.carbon.2021.10.059

M3 - Article

VL - 186

SP - 501

EP - 508

JO - CARBON

JF - CARBON

SN - 0008-6223

ER -