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Register a new userDescriptors for thermal expansion in solids
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Thermal expansion in materials can be accurately modeled with careful anharmonic phonon calculations within density functional theory. However, because of interest in controlling thermal expansion and the time consumed evaluating thermal expansion properties of candidate materials, either theoretically or experimentally, an approach to rapidly identifying materials with desirable thermal expansion properties would be of great utility. When the ionic bonding is important in a material, we show that the fraction of crystal volume occupied by ions, (based upon ionic radii), the mean bond coordination, and the deviation of bond coordination are descriptors that correlate with the room-temperature coefficient of thermal expansion for these materials found in widely accessible databases. Correlation is greatly improved by combining these descriptors in a multi-dimensional fit. This fit reinforces the physical interpretation that open space combined with low mean coordination and a variety of local bond coordinations leads to materials with lower coefficients of thermal expansion, materials with single-valued local coordination and less open space have the highest coefficients of thermal expansion.
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We study negative thermal expansion (NTE) in model lattices with multiple atoms per cell and first- and second-nearest neighbor interactions using the (anharmonic) Morse potential. By exploring the phase space of neighbor distances and thermal expansion rates of the bonds, we determine the conditions under which NTE emerges. By permitting all bond lengths to expand at different rates, we find that NTE is possible without appealing to fully rigid units. Nearly constant, large-amplitude, isotropic NTE is observed up to the melting temperature in a classical molecular dynamics model of a $mathrm{ReO}_3$-like structure when the rigidity of octahedral units is almost completely eliminated. Only weak NTE, changing over to positive expansion is observed when the corner-linked octahedra are rigid, with flexible second-neighbor bonds between neighboring octahedra permitting easy rotation. We observe similar changes to thermal expansion behavior for the diamond lattice: NTE when second-neighbor interactions are weak to positive thermal expansion when second-neighbor interactions are strong. From these observations, we suggest that the only essential local conditions for NTE are atoms with low coordination numbers along with very low energies for changing bond angles relative to bond-stretching energies.
Successful modern generalized gradient approximations (GGAs) are biased toward atomic energies. Restoration of the first-principles gradient expansion for exchange over a wide range of density gradients eliminates this bias. We introduce PBEsol, a revised Perdew-Burke-Ernzerhof GGA that improves equilibrium properties of densely-packed solids and their surfaces.
Quantitative descriptions of the structure-thermal property correlation have been a bottleneck in designing materials with superb thermal properties. In the past decade, the first-principles phonon calculations using density functional theory and the Boltzmann transport equation have become a common practice for predicting the thermal conductivity of new materials. However, first-principles calculations are too costly for high-throughput material screening and multi-scale structural design. First-principles calculations also face several fundamental challenges in modeling thermal transport properties, e.g., of crystalline materials with defects, of amorphous materials, and for materials at high temperatures. In the past five years, machine learning started to play a role in solving these challenges. This review provides a comprehensive summary and discussion on the state-of-the-art, future opportunities, and the remaining challenges in implementing machine learning for studying thermal conductivity. After an introduction to the working principles of machine learning and descriptors of material structures, recent research using machine learning to study thermal transport is discussed. Three major applications of machine learning for predicting thermal properties are discussed. First, machine learning is applied to solve the challenges in modeling phonon transport of crystals with defects, in amorphous materials, and at high temperatures. Machine learning is used to build high-fidelity interatomic potentials to bridge the gap between first-principles calculations and molecular dynamics simulations. Second, machine learning can be used to study the correlation between thermal conductivity and other properties for high-throughput materials screening. Finally, machine learning is a powerful tool for structural design to achieve target thermal conductance or thermal conductivity.
We provide a complete quantitative explanation for the anisotropic thermal expansion of hcp Ti at low temperature. The observed negative thermal expansion along the c-axis is reproduced theoretically by means of a parameter free theory which involves both the electron and phonon contributions to the free energy. The thermal expansion of titanium is calculated and found to be negative along the c-axis for temperatures below $sim$ 170 K, in good agreement with observations. We have identified a saddle-point Van Hove singularity near the Fermi level as the main reason for the anisotropic thermal expansion in $alpha-$titanium.
The thermal expansion at constant pressure of solid CD$_4$ III is calculated for the low temperature region where only the rotational tunneling modes are essential and the effect of phonons and librons can be neglected. It is found that in mK region there is a giant peak of the negative thermal expansion. The height of this peak is comparable or even exceeds the thermal expansion of solid N$_2$, CO, O$_2$ or CH$_4$ in their triple points. It is shown that like in the case of light methane, the effect of pressure is quite unusual: as evidenced from the pressure dependence of the thermodynamic Gr{u}neisen parameter (which is negative and large in the absolute value), solid CD$_4$ becomes increasingly quantum with rising pressure.
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