Stress effect on lattice thermal conductivity of anode material NiNB_(2)O_(6)for lithium-ion batteries

The thermal transport properties of NiNB_(2)O_(6)as anode material for lithium-ion battery and the effect of strain were studied by machine learning interatomic potential combined with Boltzmann transport equation.The results show that the lattice thermal conductivity of NiNB_(2)O_(6)along the three crystal directions[100],[010],and[001]are 0.947 W·m^(-1)·K^(-1),0.727 W·m^(-1)·K^(-1),and 0.465 W·m^(-1)·K^(-1),respectively,indicating the anisotropy of the lattice thermal conductivity of NiNB_(2)O_(6).This anisotropy of the lattice thermal conductivity stems from the significant difference of phonon group velocities in different crystal directions of NiNB_(2)O_(6).When the tensile strain is applied along the[001]crystal direction,the lattice thermal conductivity in all three directions decreases.However,when the compressive strain is applied,the lattice thermal conductivity in the[100]and[010]crystal directions is increased,while the lattice thermal conductivity in the[001]crystal direction is abnormally reduced due to the significant inhibition of compressive strain on the group velocity.These indicate that the anisotropy of thermal conductivity of NiNB_(2)O_(6)can be enhanced by the compressive strain,and reduced by the tensile strain.

Prediction of lattice thermal conductivity with two-stage interpretable machine learning

Thermoelectric and thermal materials are essential in achieving carbon neutrality. However, the high cost of lattice thermal conductivity calculations and the limited applicability of classical physical models have led to the inefficient development of thermoelectric materials. In this study, we proposed a two-stage machine learning framework with physical interpretability incorporating domain knowledge to calculate high/low thermal conductivity rapidly. Specifically, crystal graph convolutional neural network(CGCNN) is constructed to predict the fundamental physical parameters related to lattice thermal conductivity. Based on the above physical parameters, an interpretable machine learning model–sure independence screening and sparsifying operator(SISSO), is trained to predict the lattice thermal conductivity. We have predicted the lattice thermal conductivity of all available materials in the open quantum materials database(OQMD)(https://www.oqmd.org/). The proposed approach guides the next step of searching for materials with ultra-high or ultralow lattice thermal conductivity and promotes the development of new thermal insulation materials and thermoelectric materials.

Strong anharmonicity-assisted low lattice thermal conductivities and high thermoelectric performance in double-anion Mo_(2)AB_(2)(A=S,Se,Te;B=Cl,Br,I)semiconductors

The thermoelectric properties of layered Mo_(2)AB_(2)(A=S,Se,Te;B=Cl,Br,I)materials are systematically investigated by first-principles approach.Soft transverse acoustic modes and direct Mo d–Mo d couplings give rise to strong anharmonicities and low lattice thermal conductivities.The double anions with distinctly different electronegativities of Mo_(2)AB_(2)monolayers can reduce the correlation between electron transport and phonon scattering,and further benefit much to their good thermoelectric properties.Thermoelectric properties of these Mo_(2)AB_(2)monolayers exhibit obvious anisotropies due to the direction-dependent chemical bondings and transport properties.Furthermore,their thermoelectric properties strongly depend on carrier type(n-type or p-type),carrier concentration and temperature.It is found that n-type Mo_(2)AB_(2)monolayers can be excellent thermoelectric materials with high electric conductivity,σ,and figures of merit,ZT.Choosing the types of A and B anions of Mo_(2)AB_(2)is an effective strategy to optimize their thermoelectric performance.These results provide rigorous understanding on thermoelectric properties of double-anions compounds and important guidance for achieving high thermoelectric performance in multi-anion compounds.

Study of lattice thermal conductivity of alpha-zirconium by molecular dynamics simulation

The non-equilibrium molecular dynamics method is adapted to calculate the phonon thermal conductivity of alphazirconium. By exchanging velocities of atoms in different regions, the stable heat flux and the temperature gradient are established to calculate the thermal conductivity. The phonon thermal conductivities under different conditions, such as different heat exchange frequencies, different temperatures, different crystallographic orientations, and crossing grain boundary （GB）, are studied in detail with considering the finite size effect. It turns out that the phonon thermal conductivity decreases with the increase of temperature, and displays anisotropies along different crystallographic orientations. The phonon thermal conductivity in [0001] direction （close-packed plane） is largest, while the values in other two directions of [2īī0] and [01ī0] are relatively close. In the region near GB, there is a sharp temperature drop, and the phonon thermal conductivity is about one-tenth of that of the single crystal at 550 K, suggesting that the GB may act as a thermal barrier in the crystal.

Two-dimensional square-Au_(2)S monolayer:A promising thermoelectric material with ultralow lattice thermal conductivity and high power factor

The search for new two-dimensional(2 D)harvesting materials that directly convert(waste)heat into electricity has received increasing attention.In this work,thermoelectric(TE)properties of monolayer square-Au_(2)S are accurately predicted using a parameter-free ab initio Boltzmann transport formalism with fully considering the spin–orbit coupling(SOC),electron–phonon interactions(EPIs),and phonon–phonon scattering.It is found that the square-Au_(2)S monolayer is a promising room-temperature TE material with an n-type(p-type)figure of merit ZT=2.2(1.5)and an unexpected high n-type ZT=3.8 can be obtained at 600 K.The excellent TE performance of monolayer square-Au_(2)S can be attributed to the ultralow lattice thermal conductivity originating from the strong anharmonic phonon scattering and high power factor due to the highly dispersive band edges around the Fermi level.Additionally,our analyses demonstrate that the explicit treatments of EPIs and SOC are highly important in predicting the TE properties of monolayer square-Au_(2)S.The present findings will stimulate further the experimental fabrication of monolayer square-Au_(2)S-based TE materials and offer an in-depth insight into the effect of SOC and EPIs on TE transport properties.

Tailoring Carbon Distribution inα/γPhase of Ductile Iron and Its Effects on Thermal Conductivity

The effects of carbon distribution on the microstructure and thermal conductivity of ductile iron were investigated in the present study.The microstructure of as-cast and quenched ductile iron were characterized by OM and SEM.Results showed that the microstructure of as-cast ductile iron was composed of spheroidal graphite,ferrite with the volume of 80%,and a small amount of pearlite,and quenched ductile iron was composed of spheroidal graphite,coarse/fine acicular martensite(α_(M)phase)and high-carbon retained austenite(γphase).The volume fraction of retained austensite and its carbon content for direct quenched ductile iron and tepmered ductile iron were quantitatively analysed by XRD.Results revealed that carbon atoms diffused fromα_(M)phase toγphase during tempering at low temperatures,which resulted in carbon content in retainedγphase increasing from 1.2 wt%for the direct quenched sample to about 1.9 wt%for the tempered samples.Consequently,the lattice distortion was significantly reduced and gave rise to an increase of thermal conductivity for ductile iron.

Rattling-like behavior and band convergence induced ultra-low lattice thermal conductivity in MgAl_(2)Te_(4) monolayer

Inspired by the excellent stability exhibited by experimentally synthesized two-dimensional(2D)MoSi_(2)N_(4) layered material,the thermal and electronic transport,and thermoelectric(TE)properties of MgAl2Te4 monolayer are systematically investigated using the First-principles calculations and Boltzmann transport theory.The mechanical stability,dynamic stability,and thermal stability(900 K)of the MgAl_(2)Te_(4) monolayer are demonstrated,respectively.The MgAl_(2)Te_(4) monolayer exhibits a bandgap of 1.35 eV using the HSE06 functional in combination with spin-orbit coupling(SOC)effect.Band convergence in the valence band is favorable to improve the thermoelectric properties.The rattling thermal damping effect caused by the weak bonding of Mgsingle bondTe bonds in MgAl2Te4 monolayer leads to ultra-low lattice thermal conductivity(0.95/0.38 W/(m·K)@300 K along the x-/y-direction),which is further demonstrated by the phonon group velocities,phonon relaxation time,Grüneisen parameters,and scattering mechanisms.The optimal zT of 3.28 at 900 K is achieved for the p-type MgAl_(2)Te_(4) monolayer,showing the great promising prospect for the excellent p-type thermoelectric material.Our current work not only reveals the underlying mechanisms responsible for the excellent TE properties,but also elaborates on the promising thermoelectric application of MgAl_(2)Te_(4) monolayer material at high temperature.

Achieving high carrier mobility and low lattice thermal conductivity in GeTe-based alloys by cationic/anionic co-doping

TheⅣ-Ⅵcompound GeTe is considered as a promising alternative to the toxic PbTe for high-efficiency mid-temperature thermoelectric applications.However,pristine GeTe suffers from a high concentration of Ge vacancies,resulting in an excessively high hole concentration(>1×10^(21)cm^(-3)),which greatly limits its thermoelectric enhancement.To address this issue,CuBiTe_(2)alloying is introduced to increase the formation energy of Ge vacancies in GeTe,thereby inhibiting the high carrier concentration.The carrier scattering caused by the electronegativity difference between different elements is suppressed due to the similar electronegativity of Cu and Ge atoms.A relatively high hole mobility is obtained,which ultimately leads to a high power factor.Additionally,by introducing Se as an alloying element at the anionic site in GeTe,dense point defects with mass/strainfield fluctuations are induced.This contributes to the strengthening of phonon scattering,thereby reducing the lattice thermal conductivity from 1.44 W·m^(-1)·K^(-1)for pristine GeTe to 0.28 W·m^(-1)·K^(-1)for Ge_(0.95)Cu_(0.05)Bi_(0.05)Te_(0.9)Se_(0.15)compound at 623 K.

ZrNiSn-based compounds with high thermoelectric performance and ultralow lattice thermal conductivity via introduction of multiscale scattering centers

The high lattice thermal conductivity of half-Heuslers(HHs)restricts the further enhancement of their thermoelectric figure-of-merit(ZT).In this study,multiscale scattering centers,such as point defects,dislocations,and nanoprecipitates,are synchronously introduced in a n-type ZrNiSn-based HH matrix through Nb doping and Hf substitution.The lattice thermal conductivity is substantially decreased from 4.55(for the pristine ZrNiSn)to 1.8 W·m^(−1)·K^(−1) at 1123 K via phonon scattering over a broad wavelength range through the adjustment of multiscale defects.This value is close to the theoretically estimated lowest thermal conductivity.The power factor(PF)is enhanced from 3.25(for the pristine ZrNiSn)to 5.01 mW·m^(−1)·K^(−2) for Zr_(0.66)Hf_(0.30)Nb_(0.04)NiSn at 1123 K owing to the donor doping and band regulation via Nb doping and Hf substitution.This can be ascribed to the synergistic interaction between the lowering of the lattice thermal conductivity and retention of the high PF.Consequently,a ZT value of as high as 1.06 is achieved for Zr_(0.66)Hf_(0.30)Nb_(0.04)NiSn at 1123 K.This work demonstrates that these actions are effective in jointly manipulating the transport of electrons and phonons,thereby improving the thermoelectric performance through defect engineering.

Thermoelectric performance of Bi_(2)Sn_(2)Te_(6)monolayer with ultralow lattice thermal conductivity induced by hybrid bonding properties:A theoretical prediction

The crystal structure,mechanical stability,phonon dispersion,electronic transport properties and thermoelectric(TE)performance of the Bi_(2)Sn_(2)Te_(6)monolayer are assessed with the first-principles calculations and the Boltzmann transport theory.The Bi_(2)Sn_(2)Te_(6)monolayer is an indirect semiconductor with a band gap of 0.91 eV using the Heyd-Scuseria-Ernzerhof(HSE06)functional in consideration of the spin-orbit coupling(SOC)effect.The Bi_(2)Sn_(2)Te_(6)monolayer is high thermodynamically and mechanically stable by the assessments of elastic modulus,phonon dispersion curves,and ab initio molecular dynamics(AIMD)simulations.The hybrid bonding characteristics are discovered in Bi_(2)Sn_(2)Te_(6)monolayer,which is advantageous for phonon scattering.The antibonding interactions near the Fermi level weaken the chemical bonding and reduce the phonon vibrational frequency.Due to the short phonon relaxation time,strong anharmonic scattering,large Grüneisen parameter,and small phonon group velocity,an ultralow lattice thermal conductivity(0.27 W/(m·K)@300 K)is achieved for the Bi_(2)Sn_(2)Te_(6)monolayer.The optimal dimensionless figure of merit(ZT)values for the n-type and p-type Bi_(2)Sn_(2)Te_(6)monolayers are 2.68 and 1.63 at 700 K,respectively,associated with a high TE conversion efficiency of 20.01%at the same temperature.Therefore,the Bi_(2)Sn_(2)Te_(6)monolayer emerges as a promising candidate for TE material with high conversion efficiency.

Numerical Predictions of the Effective Thermal Conductivity of the Rigid Polyurethane Foam

A reconstruction method is proposed for the polyurethane foam and then a complete numerical method is developed to predict the effective thermal conductivity of the polyurethane foam. The finite volume method is applied to solve the 2D heterogeneous pure conduction. The lattice Boltzmann method is adopted to solve the 1D homogenous radiative transfer equation rather than Rosseland approximation equation. The lattice Boltzmann method is then adopted to solve 1D homogeneous conduction-radiation energy transport equation considering the combined effect of conduction and radiation. To validate the accuracy of the present method, the hot disk method is adopted to measure the effective thermal conductivity of the polyurethane foams at different temperature. The numerical results agree well with the experimental data. Then, the influences of temperature, porosity and cell size on the effective thermal conductivity of the polyurethane foam are investigated. The results show that the effective thermal conductivity of the polyurethane foams increases with temperature; and the effective thermal conductivity of the polyurethane foams decreases with increasing porosity while increases with the cell size.

Phonon relaxation and heat conduction in one-dimensional Fermi Pasta Ulam β lattices by molecular dynamics simulations

The phonon relaxation and heat conduction in one-dimensional Fermi Pasta-Ulam （FPU） β lattices are studied by using molecular dynamics simulations. The phonon relaxation rate, which dominates the length dependence of the FPU β lattice, is first calculated from the energy autoeorrelation function for different modes at various temperatures through equilibrium molecular dynamics simulations. We find that the relaxation rate as a function of wave number k is proportional to k^1.688, which leads to a N^0.41 divergence of the thermal conductivity in the framework of Green-Kubo relation. This is also in good agreement with the data obtained by non-equilibrium molecular dynamics simulations which estimate the length dependence exponent of the thermal conductivity as 0.415. Our results confirm the N^2/5 divergence in one-dimensional FPU β lattices. The effects of the heat flux on the thermal conductivity are also studied by imposing different temperature differences on the two ends of the lattices. We find that the thermal conductivity is insensitive to the heat flux under our simulation conditions. It implies that the linear response theory is applicable towards the heat conduction in one-dimensional FPU β lattices.

Predicting lattice thermal conductivity via machine learning: a mini review

Over the past few decades,molecular dynamics simulations and first-principles calculations have become two major approaches to predict the lattice thermal conductivity(κ_(L)),which are however limited by insufficient accuracy and high computational cost,respectively.To overcome such inherent disadvantages,machine learning(ML)has been successfully used to accurately predictκL in a high-throughput style.In this review,we give some introductions of recent ML works on the direct and indirect prediction ofκL,where the derivations and applications of data-driven models are discussed in details.A brief summary of current works and future perspectives are given in the end.

基于Lattice-Boltzmann方法的多孔介质真空绝热特性

作为真空绝热板的芯材,多孔介质微尺度空间形貌结构及物性参数对其绝热性能影响较大。为研究多孔介质真空下的导热性能,选择颗粒状、纤维状和泡沫状3种典型多孔介质材料,并基于Lattice-Boltzmann(LBM)提出了一种随机构造多孔介质物理模型的方法。模型中重要参数结合多孔介质电镜扫描图像处理获取。采用D3Q15LBM模型进行数值模拟,并分析了真空度及颗粒/纤维/泡孔等效直径对导热系数的影响规律。模拟与实验的对比结果揭示了多孔介质真空下的导热系数随真空度及多孔介质物性参数的变化规律。

基于Lattice-Boltzmann方法的纳米颗粒多孔介质导热特性

为研究气凝胶纳米颗粒的导热特性,提出了一种基于随机统计原理的构造气凝胶多孔介质介观尺度三维物理模型的方法。模型中颗粒空间分布、粒径分布及孔隙率可以根据实际气凝胶微尺度结构数据调整。基于所构造的物理模型,采用D3Q15LBM进行了数值模拟。分析了颗粒尺寸、孔隙率等因素对气凝胶导热性能的影响规律,即在既定孔隙率下,热导率随粒径增大而减小;既定粒径下,随孔隙率的递增热导率先下降后上升;颗粒尺寸不均匀性对热导率的影响甚大。模拟与实验结果相吻合。研究工作对优化气凝胶导热性能,提高其有效热导率的预测精度具有参考价值。

Alloy-induced reduction and anisotropy change of lattice thermal conductivity in Ruddlesden–Popper phase halide perovskites

The effective modulation of the thermal conductivity of halide perovskites is of great importance in optimizing their optoelectronic device performance.Based on first-principles lattice dynamics calculations,we found that alloying at the B and X sites can significantly modulate the thermal transport properties of 2D Ruddlesden−Popper(RP)phase halide perovskites,achieving a range of lattice thermal conductivity values from the lowest(κ_(c)=0.05 W·m^(−1)·K^(−1)@Cs_(4)AgBiI_(8))to the highest(κ_(a/b)=0.95 W·m^(−1)·K^(−1)@Cs4NaBiCl_(4)I_(4)).Compared with the pure RP-phase halide perovskites and three-dimensional halide perovskite alloys,the two-dimensional halide perovskite introduces more phonon branches through alloying,resulting in stronger phonon branch coupling,which effectively scatters phonons and reduces thermal conductivity.Alloying can also dramatically regulate the thermal transport anisotropy of RP-phase halide perovskites,with the anisotropy ratio ranging from 1.22 to 4.13.Subsequently,analysis of the phonon transport modes in these structures revealed that the lower phonon velocity and shorter phonon lifetime were the main reasons for their low thermal conductivity.This work further reduces the lattice thermal conductivity of 2D pure RP-phase halide perovskites by alloying methods and provides a strong support for theoretical guidance by gaining insight into the interesting phonon transport phenomena in these compounds.

Grain boundaries induce significant decrease in lattice thermal conductivity of CdTe

Semiconductors are promising in photoelectric and thermoelectric devices, for which the thermal transport properties are of particular interest. However, they have not been fully understood, especially when crystalline imperfections are present. Here, using cadmium telluride (CdTe) as an example, we illustrate how grain boundaries (GBs) affect the thermal transport properties of semiconductors. We develop a machine-learning force field from density functional theory calculations for predicting the lattice thermal conductivity (LTC) via equilibrium molecular dynamics simulations. The LTC of crystalline CdTe decreases with the relationship of κL~1/T in the simulation temperature range of 300 – 900 K, in which the isotropic LTC decreases from 3.34 to 0.23 W/ (m⋅K) due to the enhanced anharmonicity. More important, after introducing GBs, the LTC is suppressed in all directions, especially in the direction normal to the GB planes. More severe LTC suppression occurs in CdTe with Σ9 GB than that with Σ3 GB at 300 K, decreasing by 92.8% and 61.4% along the direction normal to the GB planes compared to the isotropic LTC of the crystalline CdTe, respectively. The decreased LTC is consistent with the weaker bonding near GB planes and lower shear modulus of the defective material. The analyses of the phonon dispersion curves, vibrational density of states, and phonon participation ratio indicate that the decreased LTC mainly arises from phonon scattering at GBs. Overall, our work highlights that GBs can greatly influence the LTC of semiconductors, thus providing a promising approach for thermal property design.

Screening outstanding mechanical properties and low lattice thermal conductivity using global attention graph neural network

Mechanical and thermal properties of materials are extremely important for various engineering and scientific fields such as energy conversion and energy storage.However,the characterization of these properties via high throughput screening at the quantum level,although highly accurate,is inefficient and very time-and resource-consuming.In contrast,prediction at the classical level is highly efficient but less accurate.We deploy scalable global attention graph neural network for accurate prediction of mechanical properties which bridge the gap between the accuracy at the quantum level and efficiency at the classical level.Using 10,158 elastic constants as training data,we trained the models on 5 mechanical properties,namely bulk modulus,shear modulus,Young’s modulus,Poisson’s ratio,and hardness.With the trained model,we predicted 775,947 data in search of materials with ultrahigh hardness.We further verify the recommended ultrahigh hardness materials by high precision first principles calculations,and we finally identify 20 structures with extreme hardness close to diamond,the hardest material in nature.Among those,two super hard materials are completely new and have not been reported in literature so far.We further recommend potential materials from bulk modulus prediction to search low lattice thermal conductivity,and we verify the thermal conductivity of 338 structures with first principles.Our results demonstrate that one can find materials with extreme mechanical properties recommended by graph neural network and low thermal conductivity material from bulk modulus prediction with minimal first principles calculations of the structures(only 0.04%)in the large-scale materials pool.

基于Lattice Boltzmann方法的1-3型复合材料热传导特征

基于玻尔兹曼输运理论及相关的格子玻尔兹曼方法对1-3型复合材料的热传导特征以及等效的热传导系数进行了详细研究。首先给出了计算热传导问题的格子玻尔兹曼法二维九速度不可压缩热模型以及边界条件(恒温、绝热)的处理方式,并在此基础上探讨了圆形、椭圆形、矩形和随机夹杂的1-3型复合材料的传热特征和等效热导率等,展示了给定时刻下,结构上的温度分布特征以及结构上某点温度随时间的变化特征等。数值结果表明:尽管夹杂截面形状对等效热导率有一定的影响,组分仍是等效热导率最主要的影响因素。

基于Lattice-Boltzmann方法的填充型复合导热材料的导热性能数值分析

陶瓷被广泛用作催化燃烧的催化剂载体。为及时将反应热导出,避免催化剂烧结、失活,需提高陶瓷导热能力。针对通过填充高导热性的金属或无机填料的方法制成的陶瓷复合导热材料,引入二维九速度不可压格子多相Lattice-Boltzmann模型,对构造的5种规则填充形状(圆形、正八边形、正六边形、正四边形和正三角形)的陶瓷基复合材料进行了完整的二维导热过程的数值分析。结果表明,在相同的填料和填充体积分数下,三角形填充的复合材料有效导热系数最大,填充形状愈趋近圆形则有效导热系数越小。同时模拟了各填充形状下,填充材料与基体材料的导热系数之比kp/km对复合材料有效导热系数的影响。研究结果表明,孤立的导热填料对于复合材料导热系数的提高作用是有限的,当kp/km增大到一定程度,有效导热系数不再明显增加。