Gaussian Approximation Potentials for Accurate Thermal Properties of Two-Dimensional Materials

Journal Article · Thu May 18 00:00:00 EDT 2023 · Nanoscale

DOI:https://doi.org/10.1039/D3NR00399J· OSTI ID:2005174

Kocabaş, Tuğbey; Keceli, Murat; Vazquez-Mayagoitia, Alvaro; Sevik, Cem

Two-dimensional materials (2DMs) continue to attract a lot of attention, particularly for their extreme flexibility and superior thermal properties. Molecular dynamics simulations are among the most powerful methods for computing these properties, but their reliability depends on the accuracy of interatomic interactions. While first principles approaches provide the most accurate description of interatomic forces, they are computationally expensive. In contrast, classical force fields are computationally efficient, but have limited accuracy in interatomic force description. Machine learning interatomic potentials, such as Gaussian Approximation Potentials, trained on density functional theory (DFT) calculations offer a compromise by providing both accurate estimation and computational efficiency. In this work, we present a systematic procedure to develop Gaussian approximation potentials for selected 2DMs, graphene, buckled silicene, and h-XN (X = B, Al, and Ga, as binary compounds) structures. We validate our approach through calculations that require various levels of accuracy in interatomic interactions. The calculated phonon dispersion curves and lattice thermal conductivity, obtained through harmonic and anharmonic force constants (including fourth order) are in excellent agreement with DFT results. HIPHIVE calculations, in which the generated GAP potentials were used to compute higher-order force constants instead of DFT, demonstrated the first-principles level accuracy of the potentials for interatomic force description. Molecular dynamics simulations based on phonon density of states calculations, which agree closely with DFT-based calculations, also show the success of the generated potentials in high-temperature simulations.

• Research Organization: Argonne National Laboratory (ANL), Argonne, IL (United States)
• Sponsoring Organization: Argonne National Laboratory - Laboratory Directed Research and Development (LDRD); Scientific and Technological Research Council of Turkey (TUBITAK)
• DOE Contract Number: AC02-06CH11357
• OSTI ID: 2005174
• Journal Information: Nanoscale, Vol. 15, Issue 19
• Country of Publication: United States
• Language: English

Related Subjects

2D Materials
Gaussian Approximation
Graphene
Machine Learning
Potentials
Thermoelectric Properties
Two-dimensional Materials