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Author |
Kocabas, T.; Keceli, M.; Vazquez-Mayagoitia, A.; Sevik, C. |
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Title |
Gaussian approximation potentials for accurate thermal properties of two-dimensional materials |
Type |
A1 Journal article |
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Year  |
2023 |
Publication |
Nanoscale |
Abbreviated Journal |
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Volume |
15 |
Issue |
19 |
Pages |
8772-8780 |
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Keywords |
A1 Journal article; Engineering sciences. Technology; Condensed Matter Theory (CMT) |
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Abstract |
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. |
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Wos |
000976615200001 |
Publication Date |
2023-04-19 |
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Edition |
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ISSN |
2040-3364; 2040-3372 |
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Additional Links |
UA library record; WoS full record; WoS citing articles |
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Impact Factor |
6.7 |
Times cited |
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Open Access |
Not_Open_Access |
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Notes |
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Approved |
Most recent IF: 6.7; 2023 IF: 7.367 |
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Call Number |
UA @ admin @ c:irua:196722 |
Serial |
8873 |
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Author |
Demiroglu, I.; Karaaslan, Y.; Kocabas, T.; Keceli, M.; Vazquez-Mayagoitia, A.; Sevik, C. |
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Title |
Computation of the thermal expansion coefficient of graphene with Gaussian approximation potentials |
Type |
A1 Journal article |
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Year |
2021 |
Publication |
Journal Of Physical Chemistry C |
Abbreviated Journal |
J Phys Chem C |
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Volume |
125 |
Issue |
26 |
Pages |
14409-14415 |
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Keywords |
A1 Journal article; Engineering sciences. Technology; Condensed Matter Theory (CMT) |
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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. |
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Wos |
000672734100027 |
Publication Date |
2021-06-24 |
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ISSN |
1932-7447; 1932-7455 |
ISBN |
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Additional Links |
UA library record; WoS full record; WoS citing articles |
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Impact Factor |
4.536 |
Times cited |
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Open Access |
OpenAccess |
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Notes |
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Approved |
Most recent IF: 4.536 |
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Call Number |
UA @ admin @ c:irua:179850 |
Serial |
7719 |
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