Calculation Of Transport Properties Research Articles

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.

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 (>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 > 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.

Analysis of thermophysical and transport properties of nanofluids using machine learning algorithms

Accurate calculations of thermophysical properties (TPP) and fluid transport properties are crucial for solving and enhancing a myriad of heat and mass transfer issues, particularly in the field of engineering and associated disciplines. This is even more significant in the case of advanced fluids, such as nanofluids, which play a pivotal role in the progression of new technologies. The significance of TPP in heat transfer studies is undeniable, yet there is a notable scarcity of reliable TPP values for nanofluids that can be uniformly applied to numerous problems and complex industrial systems. This shortage poses a unique challenge for nanofluids. The current study introduces NanoFluid Explorer (NanoFEx), a comprehensive MATLAB-based database specifically designed to catalog the TPP of nanofluids. The database is divided into two main parts. The first part employs Gaussian regression, supported by experimental data derived from existing literature, using kernel functions commonly used for this type of analysis. The second part features a neural network, designed for efficiency, that is trained on the aforementioned database and supplemented by an additional database formed from the Gaussian-regression output. This setup allows NanoFEx to recapture TPP values for nanofluids that may be absent in the existing literature. The preliminary findings demonstrate that NanoFEx could prove to be an invaluable resource for both theoretical and experimental researchers studying heat and mass transfer in nanofluids. Moreover, NanoFEx confirms thermodynamically consistent values with any discrepancies attributed to finite-precision computations. Furthermore, NanoFEx achieved over 93% accuracy in predicting key properties. The novelty and relevance of this study stem from the comprehensiveness of the experimental results database. In summary, this research provides an extensive and, to the best of the authors' knowledge, the most reliable dataset currently available for TPP of nanofluids. This significant contribution aims to unify nanofluid TPP research and optimize experimental processes while setting a crucial standard for future nanofluid applications.

PES and transport properties of the He⋯HBr complex from kinetic theory and molecular dynamics simulations.

The ab initio intermolecular potential of the He⋯HBr van der Waals (vdW) complex was calculated at the CCSD(T)/a5zBF level of theory and expanded in terms of the orthogonal Legendre polynomials. The PES then was implemented to calculate the interaction viscosity (η12) and diffusion (D12) coefficients through classical Mason-Monchick approximation (MMA), quantum mechanical close-coupling (CC), and molecular dynamics (MD) simulations. Energy-dependent Senftleben-Beenakker (SB) cross-sections were calculated using rotationally averaged cross-sections, and Boltzmann averaging was used to reveal the temperature dependence of the SB cross-sections over the temperature range of T = 50-1000 K. The calculated transport properties from MMA are in close agreement with the CC results, especially for temperatures lower than T = 900 K. The ab initio potential data then were used to derive LJ (12,6) and Vashishta MD force fields, and the equilibrium MD simulation methods were implemented to extract η12 and D12 coefficients using Einstein formulas. It was found that the Vashishta 3-body interaction potential model shows better accuracy than the LJ (12,6) model in MD simulations of D12. For η12, however, both MD potential models are successful, and an average absolute deviation of lower than 1% was obtained when compared to the quantum mechanical CC method.

Harnessing prion-inspired amyloid self-assembly for sustainable and biocompatible proton conductivity.

Protein-based materials have emerged as promising candidates for proton-conducting biomaterials. Therefore, drawing inspiration from the amino acid composition of prion-like domains, we designed short self-assembling peptides incorporating the (X-Tyr) motif, with X representing Asn, Gly and Ser, which form fibrillar structures capable of conducting protons. In this study, we conducted an analysis of the conductivity capacity of these fibers, with a focus on temperature and frequency dependence of conductivity. The loss tangent curves data and the electrode polarization model with the Debye approximation were employed to calculate transport properties, including conductivity, diffusivity, and density of charge carriers. Results revealed the prion-like fibers can transport protons more efficiently than biomaterials and other synthetic proton conducting materials, and that a significant increase in conductivity is observed with fibrillar orientations. The temperature dependence of conductivity of the peptides, measured in wet conditions, showed conductivities following the trend σ(NY7) < σ(GY7) < σ(SY7), in all the range of temperatures studied. The Arrhenius behavior, and the activation energy associated with conductivity followed the trend: Eact (SY7) = 8.2 ± 0.6 kJ mol-1 < Eact (GY7) < 13 ± 5 kJ mol-1 < Eact (NY7) = 31 ± 7 kJ mol-1, in different range of temperatures depending of the peptide. Furthermore, the diffusion coefficient correlated with increasing temperature in GY7 and SY7 fibers for temperatures compress between 20 °C and 80 °C, while NY7 only below 60 °C. However, it is noteworthy that the diffusivity observed in the SY7 peptide is lower, compared to GY7 and NY7 presumably due to its enlarged length. This observation can be attributed to two factors: firstly, the higher conductivity values observed in SY7 compared to GY7 and NY7, and secondly, to the value of relation observed of cations present in the peptide SY7 compared with GY7 and NY7, which in turn is dependent on temperature. In light of these findings, we envision our prion-inspired nanofibers as highly efficient proton-conducting natural biopolymers that are both biocompatible and biodegradable. These properties provide the opportunity for the development of next-generation bioelectrical interfaces and protonic devices.

Evaluation of different equations of state and free-volume approaches for unified modeling of diffusion and thermodiffusion coefficients

This article is a development and testing of the previously suggested theoretical approach for simultaneous thermodynamic modeling of diffusion and thermodiffusion coefficients. The new theoretical framework allows for the calculation of transport properties with the aid of the concepts of penetration distances and emission functions, which are fully determined from thermodynamic equations that relate functions of state variables, that is, equations of state (EoS). Four underlying EoS were tested in this work, 3 different cubic EoS and PC-SAFT, with the aim of investigating the effect of the thermodynamic model on the description of experimental data. We also investigated the impact of different approaches to estimating the free volume, which is an essential part of the model. The results indicate that the application of the effective co-volume provides a superior representation of the diffusion and thermodiffusion coefficients, which should be included in the parameterization procedure for the selection of the unified model parameters. We evaluated the use of infinite dilution diffusion coefficient data and molecular dynamics simulation to validate model parameters, and discussed the possible ways to improve the model, including predicting diffusion data as a function of temperature.

DFT Calculations of Half-Metallic Ferromagnetism and Transport Properties of Cubic KCrX3 (X = Cl, Br, I) Halides for Spintronics and Energy Conversion Applications

This paper investigates the properties of cubic halides KCrX3 (X = Cl, Br, I) compounds focusing on their half-metallic (HM) ferromagnetic and thermoelectric characteristics. The analysis is performed using the Wien2k and BoltzTrap coding. The stability of our compounds in the ferromagnetic (FM) phase is established by the higher energy release compared to the antiferromagnetic (AFM) phase. The calculated value of Poisson ratio and Pugh ratio greater than their cutoff limit (0.26 and 1.75) reveal that our examined materials are ductile in nature. To examine the electronic structure, the Trans-Bhala modified Becke Johnson potential (TB- mBJ) is employed to investigate half-metallic nature. The density of states (DDS) analysis indicates significant contributions from X-p states in the valence band and Cr 3d-states in the conduction band. A comparative analysis of crystal field () and exchange (indirect (Pd) and direct (d)) energies provides insights into the primary role of electronic spin in the ferromagnetic behavior. The observed value (4 μ B) of total magnetic moment of the investigated halide perovskites suggest that they could be promising candidates for spintronic materials. Lastly, we compute the thermoelectric parameters within a temperature range of 200 K to 800 K to explore the potential application in renewable energy devices.

Theoretical study on the origin of anomalous temperature-dependent electric resistivity of ferromagnetic semiconductor

Employing Korringa–Kohn–Rostoker Green’s function methodology, our investigation elucidates the previously obscure origins of the anomalous temperature-dependent electrical resistivity behavior of (Ga,Mn)As ferromagnetic semiconductors. Phonon and magnon excitations induced by temperature effects are addressed via the coherent potential approximation, while the Kubo–Greenwood formula is employed to compute transport properties. Consequently, the anomalous temperature-dependent electrical resistivity arising from the ferromagnetic–paramagnetic transition is successfully replicated. Our examination of electronic structures and magnetic interactions reveals pivotal roles played by antisite defects and interstitial Mn atoms in governing this behavior. As this approach enables both the estimation of temperature-dependent transport properties and the assessment of underlying mechanisms from a microscopic standpoint, it holds significant potential as a versatile tool across diverse fields.

GPU acceleration of conjugate gradient method obtaining Green's function for transport-property calculation

We present a graphics processing unit (GPU) acceleration procedure for the transport-property calculation of the RSPACE code, which uses density functional theory and the real-space finite-difference method. Porting the RSPACE code to run on a GPU using Open Accelerator and libraries results in the improvement of the computational speed and efficiency compared with a central processing unit. Furthermore, we achieved a speed up of ∼ 4x compared with a single GPU and effective processing in a multi-GPU environment by parallelizing in a way that minimizes communication and synchronization. Our GPU-acceleration procedure is demonstrated by the calculation of electron-transport properties of (4,4) carbon nanotubes (CNTs) with a 5-8-5 defect for which the calculations are suitable for parallel processing on GPUs. It is found that the information loss is strongly affected by the defect geometry when electron waves propagating in CNTs are utilized as the information transfer signal.

Primitive to conventional geometry projection for efficient phonon transport calculations

The primitive Wigner-Seitz cell and corresponding first Brillouin zone (FBZ) are typically used in calculations of lattice vibrational and transport properties as they contain the smallest number of degrees of freedom and thus have the cheapest computational cost. However, in complex materials, the FBZ can take on irregular shapes where lattice symmetries are not apparent. Thus, conventional cells (with more atoms and regular shapes) are often used to describe materials, though dynamical and transport calculations are more expensive. Here we discuss an efficient anharmonic lattice dynamic method that maps conventional cell dynamics to primitive cell dynamics based on translational symmetries. Such symmetries have not been utilized in typical lattice dynamical calculations. This leads to phase interference conditions that act like conserved quantum numbers and a conservation rule for phonon scattering that is hidden in conventional dynamics which significantly reduces the computational cost. We demonstrate this method for phonon transport in a variety of materials with inputs from first-principles calculations and attribute its efficiency to reduced scattering phase space and fewer summations in scattering matrix element calculations.