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Register a new userExploring low lattice thermal conductivity materials using chemical bonding principles
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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.
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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.
The anharmonicity of phonons in solid is ultimately rooted in the chemical bonding. However, the direct connection between phonon anharmoncity and chemical bonding is difficult to make experimentally or theoretically, due mainly to their complicated lattice structures. Here, with the help of density functional theory based calculations, we discovery that electrostatic inter-layer coupling in Bi$_2$O$_2$X (X=S,Se,Te) leads to intrinsically low lattice thermal conductivity. We explain our discovery by the strong anharmonic chemical bonding between Bi and chalcogen atoms. Our results shed light on the connection between inter-layer chemical bonding and phonon anharmonicity, which could be explored in a wide range of layered materials.
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.
An increasing number of two-dimensional (2D) materials have already been achieved experimentally or predicted theoretically, which have potential applications in nano- and opto-electronics. Various applications for electronic devices are closely related to their thermal transport properties. In this work, the strain dependence of phonon transport in monolayer SiC with a perfect planar hexagonal honeycomb structure is investigated by solving the linearized phonon Boltzmann equation. It is found that room-temperature lattice thermal conductivity ($kappa_L$) of monolayer SiC is two orders of magnitude lower than that of graphene. The low $kappa_L$ is due to small group velocities and short phonon lifetimes, which can also be explained by polarized covalent bond due to large charge transfer from Si to C atoms. In considered strain range, it is proved that the SiC monolayer is mechanically and dynamically stable. With increased tensile strain, the $kappa_L$ of SiC monolayer shows an unusual nonmonotonic up-and-down behavior, which is due to the competition between the change of phonon group velocities and phonon lifetimes of low frequency phonon modes. At low strains ($<$8%), the phonon lifetimes enhancement induces the increased $kappa_L$, while at high strains ($>$8%) the reduction of group velocities as well as the decrease of the phonon lifetimes are the major mechanism responsible for decreased $kappa_L$. Our works further enrich studies on phonon transports of 2D materials with a perfect planar hexagonal honeycomb structure, and motivate farther experimental studies.
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.
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