Neutron Diffraction Measurements and First Principles Study of Thermal Motion of Atoms in Select M_{n+1}AX_n and Binary MX Transition Metal Carbide Phases
arXiv:1210.7786 · doi:10.1103/PhysRevB.86.214301
Abstract
Herein, we compare the thermal vibrations of atoms in select ternary carbides with the formula Mn+1AXn ("MAX phases," M = Ti, Cr; A = Al, Si, Ge; X = C, N) as determined from first principles phonon calculations to those obtained from high-temperature neutron powder diffraction studies. The transition metal carbides TiC, TaC, and WC are also studied to test our methodology on simpler carbides. Good qualitative and quantitative agreement is found between predicted and experimental values for the binary carbides. For all the MAX phases studied - Ti3SiC2, Ti3GeC2, Ti2AlN, Cr2GeC and Ti4AlN3 - density functional theory calculations predict that the A element vibrates with the highest amplitude and does so anisotropically with a higher amplitude within the basal plane, which is in line with earlier results from high-temperature neutron diffraction studies. In some cases, there are quantitative differences in the absolute values between the theoretical and experimental atomic displacement parameters, such as reversal of anisotropy or a systematic offset of temperature-dependent atomic displacement parameters. The mode-dependent Grüneisen parameters are also computed to explore the anharmonicity in the system.
References in corpus (1)
Cited by in corpus (5)
- High-temperature neutron diffraction and first-principles study of temperature-dependent crystal structures and atomic vibrations in Ti3AlC2, Ti2AlC, and Ti5Al2C3
- Spin-lattice couplings in two-dimensional CrI from first-principles study
- Lattice thermal expansion and anisotropic displacements in α-sulfur from diffraction experiments and first-principles theory
- Lattice thermal expansion and anisotropic displacements in urea, bromomalonic aldehyde, pentachloropyridine and naphthalene
- A Cartesian Encoding Graph Neural Network for Crystal Structures Property Prediction: Application to Thermal Ellipsoid Estimation