Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userTopological descriptor of thermal conductivity in amorphous materials
252
0
0.0
(
0
)
Ask ChatGPT about the research
No Arabic abstract
Quantifying the correlation between the complex structures of amorphous materials and their physical properties has been a long-standing problem in materials science. In amorphous Si, a representative covalent amorphous solid, the presence of a medium-range order (MRO) has been intensively discussed. However, the specific atomic arrangement corresponding to the MRO and its relationship with physical properties, such as thermal conductivity, remain elusive. Here, we solve this problem by combining topological data analysis, machine learning, and molecular dynamics simulations. By using persistent homology, we constructed a topological descriptor that can predict the thermal conductivity. Moreover, from the inverse analysis of the descriptor, we determined the typical ring features that correlated with both the thermal conductivity and MRO. The results provide an avenue for controlling the material characteristics through the topology of nanostructures.
rate research
Read More
Lithium-intercalated layered transition-metal oxides, LixTMO2, brought about a paradigm change in rechargeable batteries in recent decades and show promise for use in memristors, a type of device for future neural computing and on-chip storage. Thermal transport properties, although being a crucial element in limiting the charging/discharging rate, package density, energy efficiency, and safety of batteries as well as the controllability and energy consumption of memristors, are poorly managed or even understood yet. Here, for the first time, we employ quantum calculations including high-order lattice anharmonicity and find that the thermal conductivity k of LixTMO2 materials is significantly lower than hitherto believed. More specifically, the theoretical upper limit of k of LiCoO2 is 6 W/m-K, 2-6 times lower than the prior theoretical predictions. Delithiation further reduces k by 40-70% for LiCoO2 and LiNbO2. Grain boundaries, strains, and porosity are yet additional causes of thermal-conductivity reduction, while Li-ion diffusion and electrical transport are found to have only a minor effect on phonon thermal transport. The results elucidate several long-standing issues regarding the thermal transport in lithium-intercalated materials and provide guidance toward designing high-energy-density batteries and controllable memristors.
Microparticles including paraffin are currently used for textiles coating in order to deaden thermal shocks. We will show that polymer nanoparticles embedded in those microsized capsules allow for decreasing the thermal conductivity of the coating and enhance the protection in the stationary regime. A reasonable volume fraction of polymer nanoparticles reduces the conductivity more than predicted by Maxwell mixing rules. Besides, measurements prove that the polymer nanoparticles do not affect the latent heat and even improve the phase change behaviour as well as the mechanical properties.
The high breakdown current densities and resilience to scaling of the metallic transition metal trichalcogenides TaSe3 and ZrTe3 make them of interest for possible interconnect applications, and it motivates this study of their thermal conductivities and phonon properties. These crystals consist of planes of strongly bonded one-dimensional chains more weakly bonded to neighboring chains. Phonon dispersions and the thermal conductivity tensors are calculated using density functional theory combined with an iterative solution of the phonon Boltzmann transport equation. The phonon velocities and the thermal conductivities of TaSe3 are considerably more anisotropic than those of ZrTe3. The maximum LA velocity in ZrTe3 occurs in the cross-chain direction, and this is consistent with the strong cross-chain bonding that gives rise to large Fermi velocities in that direction. The thermal conductivities are similar to those of other metallic two-dimensional transition metal dichalcogenides. At room temperature, a significant portion of the heat is carried by the optical modes. In the low frequency range, the phonon lifetimes and mean free paths in TaSe3 are considerably shorter than those in ZrTe3. The shorter lifetimes in TaSe3 are consistent with the presence of lower frequency optical branches and zone-folding features in the acoustic branches that arise due to the doubling of the TaSe3 unit cell within the plane.
Semiconductors with very low lattice thermal conductivities are highly desired for applications relevant to thermal energy conversion and management, such as thermoelectrics and thermal barrier coatings. Although the crystal structure and chemical bonding are known to play vital roles in shaping heat transfer behavior, material design approaches of lowering lattice thermal conductivity using chemical bonding principles are uncommon. In this work, we present an effective strategy of weakening interatomic interactions and therefore suppressing lattice thermal conductivity based on chemical bonding principles and develop a high-efficiency approach of discovering low $kappa_{rm L}$ materials by screening the local coordination environments of crystalline compounds. The followed first-principles calculations uncover 30 hitherto unexplored compounds with (ultra)low lattice thermal conductivities from thirteen prototype crystal structures contained in the inorganic crystal structure database. Furthermore, we demonstrate an approach of rationally designing high-performance thermoelectrics by additionally incorporating cations with stereochemically active lone-pair electrons. Our results not only provide fundamental insights into the physical origin of the low lattice thermal conductivity in a large family of copper-based compounds but also offer an efficient approach to discovery and design materials with targeted thermal transport properties.
We demonstrate that a high-dimensional neural network potential (HDNNP) can predict the lattice thermal conductivity of semiconducting materials with an accuracy comparable to that of density functional theory (DFT) calculation. After a training procedure based on the force, the root mean square error between the forces predicted by the HDNNP and DFT is less than 40 meV/{AA}. As typical examples, we present the results for Si and GaN bulk crystals. The deviation from the thermal conductivity calculated using DFT is within 1% at 200 to 500 K for Si and within 5.4% at 200 to 1000 K for GaN.
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
