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.

Marine Mobile Wireless Channel Modeling Based on Improved Spatial Partitioning Ray Tracing

In 5G era,it is expected to achieve wireless network coverage including offshore areas.Modeling of marine wireless channels is the basis of constructing a marine communication system.In this paper,a communication scene between an unmanned aerial vehicle(UAV)and a boat is simulated to study the marine wireless channel.Firstly,an improved spatial partitioning ray tracing algorithm is proposed to track the propagation path of electromagnetic waves at sea surface.Secondly,a mobile channel is simulated and modeled based on the track results.Finally,a loss measurement is carried out in the coastal waters based on the simple wireless channel loss measuring platform,and a path loss propagation model is built.Then we compare the actual measurement data with the simulation results and find that the two are have good consistency,which further verifies the reliability of the simulation.

Directional Design of Materials Based on Multi-Objective Optimization:A Case Study of Two-Dimensional Thermoelectric SnSe

The directional design of functional materials with multi-objective constraints is a big challenge,in which performance and stability are determined by a complicated interconnection of different physical factors.We apply multi-objective optimization,based on the Pareto Efficiency and Particle-Swarm Optimization methods,to design new functional materials directionally.As a demonstration,we achieve the thermoelectric design of 2D SnSe materials via the above methods.We identify several novel metastable 2D SnSe structures with simultaneously lower free energy and better thermoelectric performance in their experimentally reported monolayer structures.We hope that the results of our work on the multi-objective Pareto Optimization method will represent a step forward in the integrative design of future multi-objective and multi-functional materials.

Structural-functional unit ordering for high-performance electron-correlated materials

Electron-correlated materials have been drawing ever-increasing attention due to their fascinating physical behaviors and extensive application scenarios.In this review,a new method for material research and design(R&D),named structural-functional unit ordering(SFU ordering),which is presented,overcomes the shortcomings—for example,the limitation of finite chemical elements and long R&D circle-of conventional strategy and thus provides guidance for the design of these high-performance functional materials on demand.Meanwhile,with the development of material characterization technologies,SFUs of different scales and types can be directly observed,which,moreover,regulate the corresponding orderings.The review,starts with an introduction of the profile for SFU ordering and the synergistic effect between SFUs.Then,studies on several new high-performance electronic-correlated materials,for example,a ferromagnetic semiconductor with local spin,ferromagnetic metals with spin topologies,ferroelectric thin films with polar topologies,piezoelectric thin films with nanopillars enclosed by charged boundaries,thermoelectric materials with local ferromagnetic nanoparticles and topotactic phase transformation with conducting nanofilaments are stated in detail one by one.The vital aspect is the breaking of local symmetry,the construction,the structure,of SFUs and their orderings existing or theoretically existing,together with the enhanced/new performance.All in all,the main comments of the review tend to the remaining challenges,promising design approaches for the SFUs,and their orderings for high-performance functional materials.

Band degeneracy enhanced thermoelectric performance in layered oxyselenides by first-principles calculations

Band degeneracy is effective in optimizing the power factors of thermoelectric(TE)materials by enhancing the Seebeck coefficients.In this study,we demonstrate this effect in model systems of layered oxyselenide family by the density functional theory(DFT)combined with semi-classical Boltzmann transport theory.TE transport performance of layered LaCuOSe and BiCuOSe are fully compared.The results show that due to the larger electrical conductivities caused by longer electron relaxation times,the n-type systems show better TE performance than p-type systems for both LaCuOSe and BiCuOSe.Besides,the conduction band degeneracy of LaCuOSe leads to a larger Seebeck coefficient and a higher optimal carrier concentration than n-type BiCuOSe,and thus a higher power factor.The optimal figure of merit(ZT)value of 1.46 for n-type LaCuOSe is 22%larger than that of 1.2 for n-type BiCuOSe.This study highlights the potential of wide band gap material LaCuOSe for highly efficient TE applications,and demonstrates that inducing band degeneracy by cations substitution is an effective way to enhance the TE performance of layered oxyselenides.