Computation of the Thermal Expansion Coefficient of Graphene with Gaussian Approximation Potentials

Journal Article · Thu Jun 24 00:00:00 EDT 2021 · Journal of Physical Chemistry. C

DOI:https://doi.org/10.1021/acs.jpcc.1c01888· OSTI ID:1826087

Demiroğlu, İlker ^[1]; Karaaslan, Yenal ^[1]; Kocabaş, Tuğbey ^[1]; Keçeli, Murat ^[2]; Vázquez-Mayagoitia, Álvaro ^[2]; Sevik, Cem ^[3]

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1. Eskisehir Technical University (Turkey)
2. Argonne National Lab. (ANL), Lemont, IL (United States)
3. Eskisehir Technical University (Turkey); Univ. of Antwerp (Belgium)

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 work, 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.

• Research Organization: Argonne National Laboratory (ANL), Argonne, IL (United States). Laboratory Computing Resource Center (LCRC)
• Sponsoring Organization: USDOE Office of Science (SC); USDOE Laboratory Directed Research and Development (LDRD) Program; Scientific and Technological Research Council of Turkey (TUBITAK); Council of Higher Education of Turkey
• Grant/Contract Number: AC02-06CH11357
• OSTI ID: 1826087
• Journal Information: Journal of Physical Chemistry. C, Journal Name: Journal of Physical Chemistry. C Journal Issue: 26 Vol. 125; ISSN 1932-7447
• Publisher: American Chemical Society
• Country of Publication: United States
• Language: English

Related Subjects

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY
Density functional theory
Phonons
Thermal expansion
Thermodynamic modeling
Two dimensional materials