Aperiodic Superlattices Research Articles

Phonon transmission and localization in disordered side branching graphene aperiodic lattice

Blocking phonon transport via localized resonance is a crucial method for controlling heat transfer and enhancing thermoelectric performance in nanostructures. However, the effects of disorder and asymmetrically distributed side branches on thermal transport and local resonant hybridization in two-dimensional materials remain insufficiently understood. In this work, we investigate the influence of symmetric and asymmetric disordered side branches on phonon transport in branching graphene superlattices. Our results demonstrate that aperiodic superlattices (ap-SL) can reduce thermal conductivity by up to 21% compared to periodic superlattices. The reduction in thermal conductivity in ap-SL is primarily due to phonon Anderson localization caused by disordered side branches. Interestingly, the localization lengths of symmetric and asymmetric ap-SLs are comparable, resulting in similar thermal conductivity in both cases. This finding suggests that the randomness in the upper and lower branches of asymmetric graphene superlattices does not significantly affect phonon transmission. Consequently, our work indicates that differences in symmetry between the upper and lower edge branches of graphene nanoribbons can be disregarded during experimental preparation without influencing their thermal conductivity.

Prominent phonon transmission across aperiodic superlattice through coherent mode-conversion

In both particle and wave descriptions of phonons, the dense, aperiodically arranged interfaces in aperiodic superlattices are expected to strongly attenuate thermal transport due to phonon-interface scattering or broken long-range coherence. However, non-trivial thermal conductivity is still observed in these structures. In this study, we reveal that incoherent modes propagating in the aperiodic superlattice can be converted, through interference, into coherent modes defined by an approximate dispersion relation. This conversion leads to high transmission across the aperiodic superlattice structure, which contains hundreds of interfaces, ultimately resulting in non-trivial thermal conductivity. Such incoherent-to-coherent mode-conversion behavior is extensively observed in periodic superlattices. This work suggests an effective strategy to manipulate the phonon dispersion relation through layer patterning or material choice, enabling precise control of phonon transmission across aperiodic superlattices.

Influence of spatial coherence on phonon transmission across aperiodically arranged interfaces

In this study, we employ the atomistic wave-packet method to directly simulate coherent phonon transport and scattering dynamics in an aperiodic superlattice structure with aperiodically arranged interfaces. Our investigation reveals that interference dynamics, contingent upon the relative magnitudes of phonon wavelength, spatial coherence length, and average interface spacing, profoundly influence transmission spectra and mode-conversion behavior. Particularly, longer-wavelength phonons possessing larger coherence lengths exhibit more pronounced destructive interference and mode-conversion, leading to generally lower transmission. The insights gained into coherent phonon wave physics from this study can significantly augment the analysis and design of phononic devices.

Tunneling magnetoresistance and spin-valley polarization of aperiodic magnetic silicene superlattices

Magnetic silicene superlattices (MSSLs) are versatile structures with spin-valley polarization and tunneling magnetoresistance (TMR) capabilities. However, the oscillating transport properties related to the superlattice periodicity impede stable spin-valley polarization states reachable by reversing the magnetization direction. Here, we show that aperiodicity can be used to improve the spin-valley polarization and TMR by reducing the characteristic conductance oscillations of periodic MSSLs (P-MSSLs). Using the Landauer–Büttiker formalism and the transfer matrix method, we investigate the spin-valley polarization and the TMR of Fibonacci (F-) and Thue–Morse (TM-) MSSLs as typical aperiodic superlattices. Our findings indicate that aperiodic superlattices with higher disorder provide better spin-valley polarization and TMR values. In particular, TM-MSSLs reduce considerably the conductance oscillations giving rise to two well-defined spin-valley polarization states and a better TMR than F- and P-MSSLs. F-MSSLs also improve the spin-valley polarization and TMR, however they depend strongly on the parity of the superlattice generation.

Excellent thermoelectric properties of monolayer MoS2-MoSe2 aperiodic superlattices

Effective regulation of phonon thermal conductivity is crucial for the understanding of phonon transmission and improving thermoelectric (TE) performance. Using density functional theory (DFT) combined with the non-equilibrium Green's function (NEGF) method, we investigated the effect of phonon localization induced by aperiodic superlattices on the TE transport properties of superlattices based on monolayer MoS2-MoSe2. The results show that the structural defects obtained by changing the superlattice arrangement can effectively modulate the phonon thermal conductivity independently. In addition, the TE properties of the superlattices increase with temperature and central region length. The maximum value of the TE figure of merit of the aperiodic superlattice can reach 1.38 at 500 K. This implies a new potential strategy for tuning the TE performance of devices by exploiting the arrangements of the aperiodic superlattice.

High-Throughput Screening of Aperiodic Superlattices Based on Atomistic Simulation-Informed Effective Medium Theory and Genetic Algorithm

Superlattices (SLs) have received great attention as thermoelectric materials because phonon transport in them can be tailored independently without changing the electronic properties to enhance the thermoelectric figure of merit and conversion efficiency. Heterogeneous aperiodic SLs (ap-SLs) have lower thermal conductivity than their periodic counterparts (p-SL) due to Anderson localization of coherent phonons. Due to enormous amounts of layered structure combinations in ap-SLs, it is challenging to efficiently screen through all the combinations for the structure with the minimum thermal conductivity. In this work, a modified effective medium theory (m-EMT) is established to predict the thermal conductivity of Si/Ge p-SLs and ap-SLs. The effects of Anderson localization and layer thickness distribution are considered using a correction function for the degree of randomization (DOR) in layer thickness. The combination of the m-EMT and the genetic algorithm enables high-throughput screening of enormous amounts (∼ 2n, where n is the number of atomic layers) of SL structures with micron-scale total SL thickness. The thermal conductivities of ap-SLs can be constantly reduced to 1.5 W/(m·K) at average periodic layer thicknesses of 2.0 nm and a DOR of 0.8, regardless of the total SL thicknesses. Phonon spatial localization energy distribution further reflects the intense Anderson localization at interfaces, especially for those sandwiched between the highly asymmetric thick and thin layers. This work provides a high-throughput screening tool to theoretically design micron-scale and heterogeneous SLs, with detailed structural guidance for experimentally growing ap-SL with minimum thermal conductivities.

Unexpected thermal conductivity enhancement in aperiodic superlattices discovered using active machine learning

While machine learning (ML) has shown increasing effectiveness in optimizing materials properties under known physics, its application in discovering new physics remains challenging due to its interpolative nature. In this work, we demonstrate a general-purpose adaptive ML-accelerated search process that can discover unexpected lattice thermal conductivity (κl) enhancement in aperiodic superlattices (SLs) as compared to periodic superlattices, with implications for thermal management of multilayer-based electronic devices. We use molecular dynamics simulations for high-fidelity calculations of κl, along with a convolutional neural network (CNN) which can rapidly predict κl for a large number of structures. To ensure accurate prediction for the target unknown SLs, we iteratively identify aperiodic SLs with structural features leading to locally enhanced thermal transport and include them as additional training data for the CNN. The identified structures exhibit increased coherent phonon transport owing to the presence of closely spaced interfaces.

High-Throughput Screening of Aperiodic Superlattices Based on Atomistic Simulation-Informed Effective Medium Theory and Genetic Algorithm

High-Throughput Screening of Aperiodic Superlattices Based on Atomistic Simulation-Informed Effective Medium Theory and Genetic Algorithm

Propagation of electromagnetic waves on quasiperiodic rare-earth multilayers

We analyze the reflection, transmission, and absorption spectra of electromagnetic waves in the visible frequency region, where a beam focuses perpendicularly and obliquely from a transparent medium (vacuum or air) on an aperiodic superlattice made up of rare-earth/SiO2 materials. The layers of the superlattice (or magnonic crystals) are organized following a growth rule that depends on the sequence studied. The types of binary sequences analyzed were Fibonacci, Thue-Morse, and double-period. The material that forms layer A is silicon dioxide (SiO2), and the material that forms layer B is a rare-earth (dysprosium or holmium). To find the reflectance, we used the transfer-matrix method in these superlattices by analyzing, through the normal and oblique incidences, the reflection spectrum, making a detailed comparison of the results obtained for both incidences. Our results show that Ho-comprised superlattices have a more transparent profile, and that the percentage of reflection depends strongly on polarization (s or p) for all considered sequences.

Big-data-accelerated aperiodic Si/Ge superlattice prediction for quenching thermal conduction via pattern analysis

Thermal conductivity of material is one of the basic physical properties and plays an important role in manipulating thermal energy. In order to accelerate the prediction of material structure with desired thermal property, machine learning algorithm has been widely adopted. However, in the optimization of multivariable material structure such as different lengths or proportions, the machine learning algorithm may be required to be reconducted again and again for each variable, which will consume a lot of computing resources. Recently, it has been found that the thermal conductivity of aperiodic superlattices is closely related to the degree of the structural randomness, which can also be reflected in their local pattern structures. Inspired by these, we propose a new pattern analysis method, in which machine learning only needs to be carried out for one time, and through which the optimal structure of different variables with low thermal conductivity can be obtained. To verify the method, we compare the thermal conductivities of the structure obtained by pattern analysis, conventional machine learning, and previous literature, respectively. The pattern analysis method is validated to greatly reduce the prediction time of multivariable structure with high enough accuracy and may promote further development of material informatics.

Enhancement of the Seebeck coefficient and power factor in gated silicene superlattices induced by aperiodicity

This paper theoretically investigates the impact of aperiodic sequences in the ballistic transport and thermoelectric effect in silicene gated superlattices. In our analysis, we have implemented the well-known Fibonacci, Thue–Morse, and triadic Cantor type sequences. The transfer matrix technique and the Landauer–Bütikker formalism are used to calculate the transmission probability and the conductance, respectively. The Cutler–Mott formula is employed to estimate the Seebeck coefficient, and the thermoelectric power factor is then obtained. We found that the transmission minibands of aperiodic superlattices exhibit a much more fragmented structure in comparison to that reported in the periodic case. Consequently, the conductance curve presents a more pronounced oscillating shape, which improves the thermoelectric properties. In particular, the Seebeck coefficient has reached values up to 78.2 mV/K for Fibonacci, 233.0 mV/K for Thue–Morse, and 436.3 mV/K for Cantor. In addition, the power factor has been substantially increased, reaching peaks of approximately 8.2, 50.2, and 2.1 nW/K2 for the mentioned sequences, respectively. The best results were obtained for spindown (spinup) charge carriers in the K (K′) valley. Besides, an additional improvement is obtained by considering superior generations of the aperiodic sequences. Finally, our findings are supported through the redistribution of the density of the states, which is induced by the aperiodicity of the nanostructure as well as by the low-dimensionality of the thermoelectric device.

The dimensionality effect on phonon localization in graphene/hexagonal boron nitride superlattices

Phonon localization, a largely elusive phenomenon, has a great promise for improving current applications like thermoelectric materials and thermal barrier coatings. By freezing the out-of-plane atomic motion in graphene/hexagonal boron nitride (hBN) superlattices and random multilayers, i.e aperiodic superlattices, we are able to completely isolate the effect of the third dimension on phonon scattering and localization in 2D materials. In particular, we find much more prominent phonon heat conduction and localization when atomic motions in the third dimension are frozen. Rigorous spectral phonon transmission and scattering phase space analyses reveal that the phase-breaking anharmonic scatterings can significantly hinder the occurrence of phonon localization. Phonon participation ratio calculations further reveal that the flexural modes—arising from the degree of freedom of the third dimension—are rather extended, in contrast to the vastly localized in-plane modes in graphene/hBN random multilayers. These two factors altogether greatly obstruct the observation of coherent phonon localization in realistic materials. This work will be useful for guiding the search for nanostructures possessing significant phonon localization behaviors.

Quenching Thermal Transport in Aperiodic Superlattices: A Molecular Dynamics and Machine Learning Study.

Random multilayer (RML) structures, or aperiodic superlattices, can localize coherent phonons and therefore exhibit drastically reduced lattice thermal conductivity compared to their superlattice counterparts. The optimization of RML structures is essential for obtaining ultralow thermal conductivity, which is critical for various applications such as thermoelectrics and thermal barrier coatings. A higher degree of disorder in RMLs will lead to stronger phonon localization and, correspondingly, a lower lattice thermal conductivity. In this work, we identified several essential parameters for quantifying the disorder in layer thicknesses of RMLs. We were able to correlate these disorder parameters with thermal conductivity, as confirmed by classical molecular dynamics simulations of conceptual Lennard-Jones RMLs. Moreover, we have shown that these parameters are effective as features for physics-based machine learning models to predict the lattice thermal conductivity of RMLs with improved accuracy and efficiency.

Machine learning maximized Anderson localization of phonons in aperiodic superlattices

Nanostructuring materials to achieve ultra-low lattice thermal conductivity has proven to be extremely attractive for numerous applications such as thermoelectric energy conversion. Anderson localization of phonons due to aperiodicity can reduce thermal conductivity in superlattices, but the lower limit of thermal conductivity remains elusive due to the prohibitively large design space. In this work, we demonstrate that an intuition-based manual search for aperiodic superlattice structures (random multilayers or RMLs) with the lowest thermal conductivity yields only a local minimum, while a genetic algorithm (GA) based approach can efficiently identify the globally minimum thermal conductivity by only exploring a small fraction of the design space. Our results show that this minimum value occurs at an average RML period that is, surprisingly, smaller than the period corresponding to the minimum SL thermal conductivity. Above this critical period, scattering of incoherent phonons at interfaces is less, whereas below this period, the room for randomization becomes less, thus putting more coherent phonons out of Anderson localization and causing increased thermal conductivity. Moreover, the lower limit of the thermal conductivity occurs at a moderate rather than maximum randomness of the layer thickness. Our machine learning approach demonstrates a general process of exploring an otherwise prohibitively large design space to gain non-intuitive physical insights.

Anderson Localization Quenches Thermal Transport in Aperiodic Superlattices.

We show that aperiodic superlattices exhibit intriguing interplay between phononic coherent wave interference effects and incoherent transport. In particular, broadband Anderson localization results in a drastic thermal conductivity reduction of 98% at room temperature, providing an ultralow value of 1.3 W m^{-1} K^{-1}, and further yields an anomalously large thermal anisotropy ratio of ∼10^{2} in aperiodic Si/Ge superlattices. A maximum in the thermal conductivity emerges as an unambiguous consequence of phonon Anderson localization at a system length scale bridging the extended and localized transport regimes. The frequency-resolved picture, combined with our lattice dynamical description of Anderson localization, elucidates the rich transport characteristics in these systems and the potential of correlated disorder for sub- to few-THz phononic engineering of heat transport in thermoelectric applications.

Occurence of Characteristic Peaks in Phononic Multilayer Structures

In this paper the acoustic transmission properties of multilayer structures was analyzed. Were compared binary and aperiodic (Severin, Thue-Morse) superlattices, Calculations were performed using the Transfer Matrix Method (TMM) algorithm. As a superlattice environment in the simulation the water was used. The material used to construct the structure was a PNM-0.38PT piezoelectric. Multilayer types have been selected so that the total number of layers for a given generation is equal in all structures.

Photonic Transmission of a Circular Aperiodic Superlattice Made of GaAs and Metamaterial Equivalent of NaCl

The article analyzes the transmission properties of a circular aperiodic superlattices in the visible light range. The tests were carried out for the metamaterial equivalent of NaCl, which was defined as material A and GaAs - material B. Structure with the generation number of the L = {3, 4} circular superlattices was analyzed for P and S type of polarization. The structures were made of 250 nm and 300 nm layers thickness. The tests showed differences in transmission depending on the polarization. Also shown is the effect of increasing the thickness of the layers on the transmission.

Propagation of magnetostatic modes on aperiodic rare-earth multilayers

The localization behavior and scaling properties of magnetostatic modes propagating on magnetic aperiodic superlattices made up by rare-earth materials is analyzed. Both surface and bulk modes are obtained, considering two different aperiodic rules of stacking: Thue-Morse and Fibonacci. The magnetization is considered to be perpendicular to the superlattice's interfaces. Our model consider as magnetic materials both dysprosium and holmium, at different temperatures. A scaling behavior is observed for the allowed bulk bands, through specific ordering parameters, meaning that the allowed bands have a fractal (or multifractal) structure.

Designing Nanostructures for Phonon Transport via Bayesian Optimization

We demonstrate optimization of thermal conductance across nanostructures by developing a method combining atomistic Green's function and Bayesian optimization. With an aim to minimize and maximize the interfacial thermal conductance (ITC) across Si-Si and Si-Ge interfaces by means of Si/Ge composite interfacial structure, the method identifies the optimal structures from calculations of only a few percent of the entire candidates (over 60,000 structures). The obtained optimal interfacial structures are non-intuitive and impacting: the minimum-ITC structure is an aperiodic superlattice that realizes 50% reduction from the best periodic superlattice. The physical mechanism of the minimum ITC can be understood in terms of crossover of the two effects on phonon transport: as the layer thickness in superlattice increases, the impact of Fabry-P\'erot interference increases, and the rate of reflection at the layer-interfaces decreases. Aperiodic superlattice with spatial variation in the layer thickness has a degree of freedom to realize optimal balance between the above two competing mechanism. Furthermore, aperiodicity breaks the constructive phonon interference between the interfaces inhibiting the coherent phonon transport. The present work shows the effectiveness and advantage of material informatics in designing nanostructures to control heat conduction, which can be extended to other interfacial structures.

Effect of Spatially Correlated Disorder on Electronic Transport Properties of Aperiodic Superlattices (GaAs/AlxGa1-xAs)

Effect of Spatially Correlated Disorder on Electronic Transport Properties of Aperiodic Superlattices (GaAs/AlxGa1-xAs)