Calculation Of Transport Properties Research Articles

Transport properties of N + N, O + O, N + O, C + O, C+ + O, C + N, and C + C using an accurate treatment of nonzero spin and electronic angular momentum and including spin-orbit interaction.

We have carried out first principles calculations of transport properties of the title atom-atom systems using accurate abinitio electronic structure methods and quantum scattering. We go beyond the Born-Oppenheimer approximation and show how one can properly include the spin and orbital angular momentum of atoms in the calculations. We give the explicit transformation between coupled LS atomic states, where L is the total electron angular momentum and S is the total spin angular momentum, and the diatomic Hund's case (a) basis. We include both Coulomb spin-orbit interaction as well as the effect of the magnetic-moments of the electrons via the Breit interaction. The relations between the long-range forces of different symmetry electronic states are given for S + P and P + P asymptotes.

Combining machine-learning models with first-principles high-throughput calculations to accelerate the search for promising thermoelectric materials

From high-throughput screening of high-power-factor materials, through first-principles calculation of transport properties, to training machine-learning models for identifying good thermoelectric materials.

First-Principles Calculation of Ion Structures and Transport Properties of NaF–KF–AlF3 Low-Temperature Electrolytes at Various Temperatures

First-Principles Calculation of Ion Structures and Transport Properties of NaF–KF–AlF3 Low-Temperature Electrolytes at Various Temperatures

Machine Learning and First-Principle Predictions of Materials with Low Lattice Thermal Conductivity.

We performed machine learning (ML) simulations and density functional theory (DFT) calculations to search for materials with low lattice thermal conductivity, κL. Several cadmium (Cd) compounds containing elements from the alkali metal and carbon groups including A2CdX (A = Li, Na, and K; X = Pb, Sn, and Ge) are predicted by our ML models to exhibit very low κL values (<1.0 W/mK), rendering these materials suitable for potential thermal management and insulation applications. Further DFT calculations of electronic and transport properties indicate that the figure of merit, ZT, for the thermoelectric performance can exceed 1.0 in compounds such as K2CdPb, K2CdSn, and K2CdGe, which are therefore also promising thermoelectric materials.

Investigation of electronic, optical and thermoelectric properties in orthorhombic perovskite SmFeO3

In this work, we have investigated the electronic, optical, and thermoelectric properties of orthorhombic perovskite SmFeO3 using density functional and Boltzmann transport theories, for renewable energy applications. The modeling of the exact electron exchange-correlation has been carried out using both Generalized Gradient Approximation (GGA) and a functional modified with Hubbard interactions. The spin-polarized calculations show that SmFeO3 adopts a G-type antiferromagnetic configuration. The studied compound's electronic band structure and density of states analysis reveal its p-type semiconductor behavior. Furthermore, the optical properties such as real ε1(ω) and imaginary ε2(ω) components of the dielectric function as well as other conventional optical parameters, were investigated. The results show that SmFeO3 is a promising material for optoelectronic applications. The calculated transport properties including the Seebeck coefficient (S), the electrical conductivity (σ/τ), the electronic thermal conductivity (k0/τ) and the electronic figure of merit (ZTe) show significant results. The studied perovskite is also a potential candidate for thermoelectric applications, due to its high Seebeck coefficient, reaching 2388 μV/K, and the large electronic figure of merit which is close to 1 at room temperature. To improve the thermoelectric performance of SmFeO3 material, we have studied the effect of both p-type and n-type doping on its thermoelectric behavior at different temperature values.

Machine Learning for Predicting Ultralow Thermal Conductivity and High ZT in Complex Thermoelectric Materials.

Efficient and precise calculations of thermal transport properties and figures of merit, alongside a deep comprehension of thermal transport mechanisms, are essential for the practical utilization of advanced thermoelectric materials. In this study, we explore the microscopic processes governing thermal transport in the distinguished crystalline material Tl9SbTe6 by integrating a unified thermal transport theory with machine learning-assisted self-consistent phonon calculations. Leveraging machine learning potentials, we expedite the analysis of phonon energy shifts, higher-order scattering mechanisms, and thermal conductivity arising from various contributing factors, such as population and coherence channels. Our finding unveils an exceptionally low thermal conductivity of 0.31 W m-1 K-1 at room temperature, a result that closely correlates with experimental observations. Notably, we observe that the off-diagonal terms of heat flux operators play a significant role in shaping the overall lattice thermal conductivity of Tl9SbTe6, where the ultralow thermal conductivity resembles that of glass due to limited group velocities. Furthermore, we achieve a maximum ZT value of 3.17 in the c-axis orientation for p-type Tl9SbTe6 at 600 K and an optimal ZT value of 2.26 in the a-axis and b-axis direction for n-type Tl9SbTe6 at 500 K. The crystalline Tl9SbTe6 not only showcases remarkable thermal insulation but also demonstrates impressive electrical properties owing to the dual-degeneracy phenomenon within its valence band. These results not only elucidate the underlying reasons for the exceptional thermoelectric performance of Tl9SbTe6 but also suggest potential avenues for further experimental exploration.

A DFT Calculations of Mechanical, Optoelectronic and Transport Properties of Cubic AMgI3 (A = Li/Na) Halides for Photovoltaic and Energy Conversion Applications

A DFT Calculations of Mechanical, Optoelectronic and Transport Properties of Cubic AMgI3 (A = Li/Na) Halides for Photovoltaic and Energy Conversion Applications

Shortcut Approximation to the Coupled Equations of Heat and Electric Currents

A self-consistent approximation to the coupled equations of heat and electric current densities is developed to facilitate the integration of the relaxation time approximation of the Boltzmann transport equation in a realistic design, analysis, and optimization of energy conversion devices. By introducing two auxiliary energies in the empirical Boltzmann transport equations, the coupled equations can be simply expressed in these auxiliary energies. These auxiliary energies are then determined by optimizing both the heat current through charge neutrality equation without the presence of electric current and the carrier concentrations with the presence of electric current. The heat and the electric current densities as well as the transport properties can then be calculated. The calculated transport properties agreed reasonably well with existing experimental and computational data.

Effective Coupling Model to Treat the Odd-Even Effect on the Current-Voltage Response of Saturated Linear Carbon Chains Single-Molecule Junctions.

The calculation of the electrical charge transport properties of alkanes C n H 2n S 2 with (n = 4-11) was performed to understand the odd-even effect on its current-voltage response. The extended molecule and broadband limit models were used to describe the molecular junction and covalent coupling with the electrodes. It was shown that among the participating molecular orbitals, HOMO and HOMO-1 are the ones with the most charge transport contribution. Moreover, the odd-even effect is caused by the alternation of the eigenvalues of some frontier orbitals as a function of the number of carbons, especially the HOMO that dominates the electrical transport. It could also be noted that when the current is analyzed outside the resonance, the relationship with the number of carbons exponentially decays, confirming the reports in the literature. To the best of our knowledge, a first principle study of the odd-even effect in symmetric systems composed by linear saturated carbon chains covalently coupled to electrodes has not been reported yet.

Real-space calculation of thermal Hall effects in strained and disordered kagome ferromagnets

Ferromagnetic insulators may exhibit magnon Hall effects when subjected to a temperature gradient due to the Dzyaloshinsky–Moriya interaction. In this study, we investigate the magnon thermal Hall conductivity κxy of kagome ferromagnets in real space using the kernel polynomial method. We first establish the formalism in real space within the framework of linear response theory, which enables efficient numerical calculations of thermal transport properties under various imperfections. The validity and accuracy of the real-space approach are confirmed by comparing the calculations with those obtained in momentum space. This approach is particularly advantageous for computing the thermal transport coefficients of disordered lattices. We consider two types of disorder in kagome ferromagnets and observe that both types significantly influence κxy across the entire temperature range. This is in contrast to the effects of strains, where strains primarily impact the maximum values of κxy .

High-throughput discovery of metal oxides with high thermoelectric performance via interpretable feature engineering on small data

In this work, we have proposed a data-driven screening framework combining the interpretable machine learning with high-throughput calculations to identify a series of metal oxides that exhibit both high-temperature tolerance and high power factors. Aiming at the problem of weak generalization ability of small data with power factors at high temperatures, we employ symbolic regression for feature creation which enhances the robustness of the model while preserving the physical meaning of features. 33 candidate metal oxides are finally targeted for high-temperature thermoelectric applications from a pool of 48,694 compounds in the Materials Project database. The Boltzmann transport theory is utilized to perform electrical transport properties calculations at 1,000 K. The relaxation time is approximated by employing constant electron-phonon coupling based on the deformation potential theory. Considering band degeneracy, the electron group velocity is obtained using the momentum matrix element method, yielding 28 materials with power factors greater than 50 μWcm−1K−2. The high-throughput framework we proposed is instrumental in the selection of metal oxides for high-temperature thermoelectric applications. Furthermore, our data-driven analysis and transport calculation suggest that metal oxides rich in elements such as cerium (Ce), tin (Sn), and lead (Pb) tend to exhibit high power factors at high temperatures.

Towards the Analytical Generalization of the Transcendental Energy Equation, Group Velocity, and Effective Mass in One-Dimensional Periodic Potential Wells with a Computational Application to Common Coupled Potentials

The analytical generalization for N periodic potential wells coupled to a probe rectangular-like potential and a zero potential is extremely important in the study of one-dimensional periodic potentials in solid state physics, e.g., in the calculation of transport, optical, and magnetic properties. These findings raise the possibility of calculating equations for the generalization of N arbitrary potentials related to any potential V(x) using special functions as a solution. In this work, a novel analytical generalization of the transcendental energy equation, group velocity, and effective mass for N-coupled potentials to a probe one-dimensional potential V=V(x) was proposed. Initially, two well-known linear periodic potentials V=V(x) were employed to obtain analytical solutions for rectangular-like and Dirac-delta potentials. Python libraries were used to easily represent the equations for one or two rectangular-like potentials coupled with an arbitrary potential, highlighting the transcendental energy, group velocity, and effective mass. The results showed that the group velocity behavior changed its orientation due to the sign of the potential, whereas the width of the potential V(x) strongly influenced the group velocity behavior. The effective mass was also modified by the potential shapes, and their combinations, both effective mass and group velocity, exhibited similar physical behaviors to those found in ordinary rectangular-like potentials.

Physical properties and power conversion efficiency of SrZrX3 (X=S and Se) chalcogenide perovskite solar cell

The search for efficient substances in energy conversion devices, which are low-cost, highly stable, and not hazardous to humanity has intensified among material scientists. Here, we have investigated the chalcogenide-based metal (Sr — strontium) perovskites in the context of developing materials. We have identified the electrical and optical features of these materials using the modified Becke–Johnson potential, revealing information about their nature. With computed values of 2.009[Formula: see text]eV for SrZrS3 and 1.096[Formula: see text]eV for SrZrSe3, respectively, they have shown to be direct bandgap semiconductors. We have also found that both materials exhibit transparency to the striking photon at low energy and demonstrate absorption and optical conduction in the UV region. These materials will be useful in thermoelectric devices because the transport property calculation shows that their figure of merit is unity at both low and high temperatures. In regard to applications, we determined the spectroscopic limited maximum efficiency (SLME) of SrZrS3 (SrZrSe3) and discovered that the efficiency increases from 6.3% to 22.3% (7.9% to 32%) when the film thickness is increased from 100[Formula: see text]nm to 1[Formula: see text][Formula: see text]m at 300[Formula: see text]K, after that it stabilizes. This research shows that these materials ought to be utilized as an alert substance in the design of energy conversion products, and the proposed results are supported by experimental and other theoretical data. We suggest that these substances are strong contenders for use in power conversion equipment depending upon their optical and transport characteristics.

Screening of Complex Layered Chalcogenide Structures as High-Performance Thermoelectrics by High-Throughput Calculations

Thermoelectric materials have drawn much attention over the last two decades due to the increase in global energy demand. However, designing efficient thermoelectrics reveals itself as a tough task for their properties (Seebeck coefficient, electrical conductivity, thermal conductivity) are mutually opposed. Hence, most recently, new design approaches have appeared, among which high-throughput methods have been implemented either experimentally or computationally. In this work, a high-throughput computer program has been designed to generate over 4000 structures based on a small set of complex layered chalcogenide compounds taken from the mAIVBVI nA2VB3VI homologous series, where AIV is Ge, AV is Sb and BVI is Te. The computer-generated structures have been investigated using density-functional theory methods, and the electronic and transport properties have been calculated. It has been found, using the quantum theory of atoms in molecules and crystals, that a wide variety of bond types constitutes the bonding network of the structures. All the structures are found to have negative formation energies. Among the obtained final structures, 43 are found with a wide band gap energy (&gt;0.25 eV), 358 with semi-conductor/metal characteristics, and 731 with metallic characteristics. The transport properties calculations, using the Boltzmann equation, reveal that two p-type and 86 n-type structures are potentially promising compounds for thermoelectric applications.

High-throughput thermoelectric materials screening by deep convolutional neural network with fused orbital field matrix and composition descriptors

Thermoelectric materials harvest waste heat and convert it into reusable electricity. Thermoelectrics are also widely used in inverse ways such as refrigerators and cooling electronics. However, most popular and known thermoelectric materials to date were proposed and found by intuition, mostly through experiments. Unfortunately, it is extremely time and resource consuming to synthesize and measure the thermoelectric properties through trial-and-error experiments. Here, we develop a convolutional neural network (CNN) classification model that utilizes the fused orbital field matrix and composition descriptors to screen a large pool of materials to discover new thermoelectric candidates with power factor higher than 10 μW/cm K2. The model used our own data generated by high-throughput density functional theory calculations coupled with ab initio scattering and transport package to obtain electronic transport properties without assuming constant relaxation time of electrons, which ensures more reliable electronic transport properties calculations than previous studies. The classification model was also compared to some traditional machine learning algorithms such as gradient boosting and random forest. We deployed the classification model on 3465 cubic dynamically stable structures with non-zero bandgap screened from Open Quantum Materials Database. We identified many high-performance thermoelectric materials with ZT &amp;gt; 1 or close to 1 across a wide temperature range from 300 to 700 K and for both n- and p-type doping with different doping concentrations. Moreover, our feature importance and maximal information coefficient analysis demonstrates two previously unreported material descriptors, namely, mean melting temperature and low average deviation of electronegativity, that are strongly correlated with power factor and thus provide a new route for quickly screening potential thermoelectrics with high success rate. Our deep CNN model with fused orbital field matrix and composition descriptors is very promising for screening high power factor thermoelectrics from large-scale hypothetical structures.

Thermoelectric, mechanical, and pressure sustained half-metallic properties of BaInO3 perovskite for spintronics applications: DFT computation

Oxide perovskites continue to promote research interest because of their concurrent use in spintronic and thermoelectric applications. The electronic, magnetic, and thermoelectric properties of new half-metallic BaInO3 perovskite are investigated using the density functional theory. The structural and thermodynamic stability of the proposed perovskite is provided by the tolerance factor, octahedral factor, formation energy, and phonon dispersion curves. The structural relaxation curves reveal that the ground state is ferromagnetic. The generalized gradient approximation and mBJ band structure plots show that the half-metallicity exclusively results from the strong exchange splitting of 2p-bands at the Fermi level. Compared with PBE, mBJ depicts highly localized magnetic moments around oxygen along with enhanced half-metallic gaps and band gaps in the spin-up channel. Under a compressive strain, the system undergoes a magnetic phase transition from half-metallic ferromagnet to non-magnetic metal at 30 GPa. The elastic stability at the studied pressure range has been verified from Blackman’s and Every’s diagrams. The material remains ductile and exhibits moderate elastic anisotropy in the studied pressure range. The quasi-harmonic Debye model is employed to study the temperature and pressure effects of thermodynamic parameters. The computed transport properties including the Seebeck coefficient and spin-Seebeck coefficient predict reasonable thermoelectric performance in generating thermally induced spin-polarized current and spin current, respectively. Such a detailed study of this material could open prospects in spintronic as well as waste energy recovery devices.

Structural, electronic, optical, elastic, mechanical, thermodynamic, and thermoelectric properties of lead-free and environmentally friendly CsSnX3 (X = Cl, Br) perovskites

In this study, we systematically examined the physical properties of CsSnX3 (X = Cl, Br) by employing the (FP-LAPW) method within the (GGA) and GGA + mBJ approximations. The investigated compounds exhibit direct bandgap semiconducting with an ionic-covalent mixture in CsSnX3 bonds. Notably, CsSnCl3 and CsSnBr3 exhibit exceptional light absorption and optical conductivity, making them appropriate for a particular application. Elastic constants of compounds and different moduli are determined for the first time. Considering the thermodynamic model, perovskite materials consistently display thermodynamic properties such as heat capacity, Debye temperature, Grüneisen constant for a range of pressures and temperatures. The calculations of transport properties by using BoltzTrap code reveals a high figure of merit close to unity for CsSnX3 (Cl, Br) at room temperature, highlighting their potential for thermoelectric applications. Overall, CsSnX3 (Cl, Br) perovskite exhibit remarkable physical properties and environmental compatibility, positioning them as up-and-coming candidates for optoelectronic and thermoelectric applications.

Application of a multiscale approach for modeling the rheology of complex fluids in industrial mixing equipment

Many industrial sectors, like the personal care one, make wide use of mixing processes that involve complex fluids. However, modeling the rheology of these fluids is still challenging due to their non-Newtonian behavior, which depends also on the local composition. Computational tools such as dissipative particle dynamics (DPD) have been already used to calculate the equilibrium properties of these systems. Moreover, different works have been focused on the calculation of transport properties from these mesoscale DPD simulations. Multiscale approaches have been proposed to couple rheological information from DPD with computational fluid dynamics (CFD) simulations. The CFD technique reproduces the macroscale piece of equipment, implementing a rheology model built using the Gaussian process regression, a mathematical tool related to machine learning. In this work, such a framework is tested on an industrial process, to assess its performance on a realistic application. The investigated system is a solution at a high concentration of sodium lauryl ether sulfate in water under laminar fluid dynamics regime. The results show that the mixture correctly exhibits a shear-thinning behavior and presents viscosity values in good agreement with rheology experiments. While the feasibility of the coupling approach is shown, further studies on DPD are needed to improve the accuracy and the predictability of the methodology.

Phonon transport properties of Janus Pb2 XAs (X = P, Sb, and Bi) monolayers: A DFT study

Grasping the underlying mechanisms behind the low lattice thermal conductivity of materials is essential for the efficient design and development of high-performance thermoelectric materials and thermal barrier coating materials. In this paper, we present a first-principles calculations of the phonon transport properties of Janus Pb2PAs and Pb2SbAs monolayers. Both materials possess low lattice thermal conductivity, at least two orders of magnitude lower than graphene and h-BN. The room temperature thermal conductivity of Pb2SbAs (0.91 W/mK) is only a quarter of that of Pb2PAs (3.88 W/mK). We analyze in depth the bonding, lattice dynamics, and phonon mode level information of these materials. Ultimately, it is determined that the synergistic effect of low group velocity due to weak bonding and strong phonon anharmonicity is the fundamental cause of the intrinsic low thermal conductivity in these Janus structures. Relative regular residual analysis further indicates that the four-phonon processes are limited in Pb2PAs and Pb2SbAs, and the three-phonon scattering is sufficient to describe their anharmonicity. In this study, the thermal transport properties of Janus Pb2PAs and Pb2SbAs monolayers are illuminated based on fundamental physical mechanisms, and the low lattice thermal conductivity endows them with the potential applications in the field of thermal barriers and thermoelectrics.

The MatHub‐3d first‐principles repository and the applications on thermoelectrics

AbstractFollowing the Materials Genome Initiative project, materials research has embarked a new research paradigm centered around material repositories, significantly accelerating the discovery of novel materials, such as thermoelectrics. Thermoelectric materials, capable of directly converting heat into electricity, are garnering increasing attention in applications like waste heat recovery and refrigeration. To facilitate research in this emerging paradigm, we have established the Materials Hub with Three‐Dimensional Structures (MatHub‐3d) repository, which serves as the foundation for high‐throughput (HTP) calculations, property analysis, and the design of thermoelectric materials. In this review, we summarize recent advancements in thermoelectric materials powered by the MatHub‐3d, specifically HTP calculations of transport properties and material design on key factors. For HTP calculations, we develop the electrical transport package for HTP purpose, and utilize it for materials screening. In some works, we investigate the relationship between transport properties and chemical bonds for particular types of thermoelectric compounds based on HTP results, enhancing the fundamental understanding about interested compounds. In our work associated with material design, we primarily utilize key factors beyond transport properties to further expedite materials screening and speedily identify specific materials for further theoretical/experimental analyses. Finally, we discuss the future developments of the MatHub‐3d and the evolving directions of database‐driven thermoelectric research.