Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.13087/663
Title: Computation of the Thermal Expansion Coefficient of Graphene with Gaussian Approximation Potentials
Authors: Demiroğlu, İlker
Karaaslan, Yenal
Kocabaş, Tuğbey
Keçeli, Murat
Vazquez-Mayagoitia, Alvaro
Sevik, Cem
Issue Date: 2021
Publisher: Amer Chemical Soc
Abstract: Direct experimental measurement of thermal expansion coefficient without substrate effects is a challenging task for two-dimensional (2D) materials, and its accurate estimation with large-scale ab initio molecular dynamics is computationally very expensive. Machine learning-based interatomic potentials trained with ab initio data have been successfully used in molecular dynamics simulations to decrease the computational cost without compromising the accuracy. In this study, we investigated using Gaussian approximation potentials to reproduce the density functional theory-level accuracy for graphene within both lattice dynamical and molecular dynamical methods, and to extend their applicability to larger length and time scales. Two such potentials are considered, GAP17 and GAP20. GAP17, which was trained with pristine graphene structures, is found to give closer results to density functional theory calculations at different scales. Further vibrational and structural analyses verify that the same conclusions can be deduced with density functional theory level in terms of the reasoning of the thermal expansion behavior, and the negative thermal expansion behavior is associated with long-range out-of-plane phonon vibrations. Thus, it is argued that the enabled larger system sizes by machine learning potentials may even enhance the accuracy compared to small-size-limited ab initio molecular dynamics.
URI: https://doi.org/10.1021/acs.jpcc.1c01888
https://hdl.handle.net/20.500.13087/663
ISSN: 1932-7447
1932-7455
Appears in Collections:Mimarlık Bölümü Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu
WoS İndeksli Yayınlar Koleksiyonu

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