arXiv subjects

Shenghong Ju

Publications and source records attributed to Shenghong Ju.

29 records · Page 2Linked to original sources

arXiv2019

Exploring diamond-like lattice thermal conductivity crystals via feature-based transfer learning

Ultrahigh lattice thermal conductivity materials hold great importance since they play a critical role in the thermal management of electronic and optical devices. Models using machine learning can search for materials with outstanding higher-order properties like thermal conductivity. However, the lack of sufficient data to train a model is a serious hurdle. Herein we show that big data can complement small data for accurate predictions when lower-order feature properties available in big data are selected properly and applied to transfer learning. The connection between the crystal information and thermal conductivity is directly built with a neural network by transferring descriptors acquired through a pre-trained model for the feature property. Successful transfer learning shows the ability of extrapolative prediction and reveals descriptors for lattice anharmonicity. Transfer learning is employed to screen over 60000 compounds to identify novel crystals that can serve as alternatives to diamond.

cond-mat.mtrl-sci↗

arXiv2019

Expanding the horizon of automated metamaterials discovery via quantum annealing

Complexity of materials designed by machine learning is currently limited by the inefficiency of classical computers. We show how quantum annealing can be incorporated into automated materials discovery and conduct a proof-of-principle study on designing complex thermofunctional metamaterials consisting of SiO2, SiC, and Poly(methyl methacrylate). Empirical computing time of our quantum-classical hybrid algorithm involving a factorization machine, a rigorous coupled wave analysis, and a D-Wave 2000Q quantum annealer was insensitive to the problem size, while a classical counterpart experienced rapid increase. Our method was used to design complex structures of wavelength selective radiators showing much better concordance with the thermal atmospheric transparency window in comparison to existing human-designed alternatives. Our result shows that quantum annealing provides scientists gigantic computational power that may change how materials are designed.

physics.app-ph↗

arXiv2019

Materials Informatics for Heat Transfer: Recent Progresses and Perspectives

With the advances in materials and integration of electronics and thermoelectrics, the demand for novel crystalline materials with ultimate high/low thermal conductivity is increasing. However, search for optimal thermal materials is challenge due to the tremendous degrees of freedom in the composition and structure of crystal compounds and nanostructures, and thus empirical search would be exhausting. Materials informatics, which combines the simulation/experiment with machine learning, is now gaining great attention as a tool to accelerate the search of novel thermal materials. In this review, we discuss recent progress in developing materials informatics for heat transport: the exploration of crystals with high/low thermal conductivity via high-throughput screening, and nanostructure design for high/low thermal conductance using the Bayesian optimization and Monte Carlo tree search. The progresses show that the materials informatics method are useful for designing thermal functional materials. We end by addressing the remaining issues and challenges for further development.

physics.app-ph↗

arXiv2018

CT Super-resolution GAN Constrained by the Identical, Residual, and Cycle Learning Ensemble(GAN-CIRCLE)

Computed tomography (CT) is widely used in screening, diagnosis, and image-guided therapy for both clinical and research purposes. Since CT involves ionizing radiation, an overarching thrust of related technical research is development of novel methods enabling ultrahigh quality imaging with fine structural details while reducing the X-ray radiation. In this paper, we present a semi-supervised deep learning approach to accurately recover high-resolution (HR) CT images from low-resolution (LR) counterparts. Specifically, with the generative adversarial network (GAN) as the building block, we enforce the cycle-consistency in terms of the Wasserstein distance to establish a nonlinear end-to-end mapping from noisy LR input images to denoised and deblurred HR outputs. We also include the joint constraints in the loss function to facilitate structural preservation. In this deep imaging process, we incorporate deep convolutional neural network (CNN), residual learning, and network in network techniques for feature extraction and restoration. In contrast to the current trend of increasing network depth and complexity to boost the CT imaging performance, which limit its real-world applications by imposing considerable computational and memory overheads, we apply a parallel $1\times1$ CNN to compress the output of the hidden layer and optimize the number of layers and the number of filters for each convolutional layer. Quantitative and qualitative evaluations demonstrate that our proposed model is accurate, efficient and robust for super-resolution (SR) image restoration from noisy LR input images. In particular, we validate our composite SR networks on three large-scale CT datasets, and obtain promising results as compared to the other state-of-the-art methods.

eess.IV↗

arXiv2018

Structure-sensitive Multi-scale Deep Neural Network for Low-Dose CT Denoising

Computed tomography (CT) is a popular medical imaging modality in clinical applications. At the same time, the x-ray radiation dose associated with CT scans raises public concerns due to its potential risks to the patients. Over the past years, major efforts have been dedicated to the development of Low-Dose CT (LDCT) methods. However, the radiation dose reduction compromises the signal-to-noise ratio (SNR), leading to strong noise and artifacts that down-grade CT image quality. In this paper, we propose a novel 3D noise reduction method, called Structure-sensitive Multi-scale Generative Adversarial Net (SMGAN), to improve the LDCT image quality. Specifically, we incorporate three-dimensional (3D) volumetric information to improve the image quality. Also, different loss functions for training denoising models are investigated. Experiments show that the proposed method can effectively preserve structural and texture information from normal-dose CT (NDCT) images, and significantly suppress noise and artifacts. Qualitative visual assessments by three experienced radiologists demonstrate that the proposed method retrieves more detailed information, and outperforms competing methods.

cs.CV↗

arXiv2018

Thermal interface conductance between aluminum and aluminum oxide: A rigorous test of atomistic level theories

We report the first ever accurate theoretical prediction of thermal conductance of any material interface. Thermal interfacial conductance of aluminum (Al)-sapphire (α-Al2O3) interface along crystal directions (111) Al || (0001) Al2O3 for temperature ranging from 50-500 K is calculated using two fundamentally different methods: interfacial conductance modal analysis (ICMA) and atomistic green function (AGF). While AGF overpredicts interfacial conductance, both the quantitative and qualitative predictions of ICMA are exceptional when compared with the time-domain thermoreflectance (TDTR) experimental data. The mean error in ICMA results are below 5%. We believe that the accurate theoretical prediction by ICMA can be credited to a more fundamental treatment of the interfacial heat flux in contrast to that of the phonon gas model (PGM) and inclusion of anharmonicity to full order. ICMA also gives the eigen mode level details revealing the nanoscale picture of heat transport: more than 90% of conductance is contributed by the cross correlation (interaction) between partially extended modes of Al and Al2O3 and the remaining is attributed to interfacial modes. This is a major milestone in combustion heat transfer research enabling materials scientists to rationally design propellant architectures to serve long-distance propulsion missions.

cond-mat.mtrl-sci↗

arXiv2018

Revisiting PbTe to identify how thermal conductivity is really limited

Due to the long range interaction in lead telluride (PbTe), the transverse optical (TO) phonon becomes soft around the Brillouin zone center. Previous studies have postulated that this zone-center softening causes the low thermal conductivity of PbTe through either enlarged phonon scattering phase space and/or strengthened lattice anharmonicity. In this work, we reported an extensive sensitivity analysis of the PbTe thermal conductivity to various factors: range and magnitude of harmonic and anharmonic interatomic force constants, and phonon wavevectors in the three-phonon scattering processes. The analysis reveals that the softening by long range harmonic interaction itself does not reduce thermal conductivity and it is the large magnitude of the anharmonic (cubic) force constants that realizes low thermal conductivity, however, not through the TO phonons around the zone center but dominantly through the ones with larger wavevectors in the middle of Brillion zone. The work clarifies that local band softening cannot be a direct finger print for low thermal conductivity and the entire Brillion zone needs to be characterized on exploring low thermal conductivity materials.

physics.comp-ph↗

arXiv2017

Phonon-interference resonance effects in nanoparticles embedded in a matrix

We report an unambiguous phonon resonance effect originating from germanium nanoparticles embedded in silicon matrix. Our approach features the combination of phonon wave-packet method with atomistic dynamics and finite element method rooted in continuum theory. We find that multimodal phonon resonance, caused by destructive interference of coherent lattice waves propagating through and around the nanoparticle, gives rise to sharp and significant transmittance dips, blocking the lower-end frequency range of phonon transport that is hardly diminished by other nanostructures. The resonance is sensitive to the phonon coherent length, where the finiteness of the wave packet width weakens the transmittance dip even when coherent length is longer than the particle diameter. Further strengthening of transmittance dips are possible by arraying multiple nanoparticles that gives rise to the collective vibrational mode. Finally, it is demonstrated that these resonance effects can significantly reduce thermal conductance in the lower-end frequency range.

physics.comp-ph↗

arXiv2017

Thermal boundary conductance across epitaxial ZnO/GaN interfaces: Assessment of phonon gas models and atomistic Green's function approaches for predicting interfacial phonon transport

We present experimental measurements of the thermal boundary conductance (TBC) from $77 - 500$ K across isolated heteroepitaxially grown ZnO films on GaN substrates. These data provide an assessment of the assumptions that drive the phonon gas model-based diffuse mismatch models (DMM) and atomistic Green's function (AGF) formalisms for predicting TBC. Our measurements, when compared to previous experimental data, suggest that the TBC can be influenced by long wavelength, zone center modes in a material on one side of the interface as opposed to the "vibrational mismatch" concept assumed in the DMM; this disagreement is pronounced at high temperatures. At room temperature, we measure the ZnO/GaN TBC as $490\lbrack +150, -110\rbrack$ MW m$^{-2}$ K$^{-1}$. The disagreement among the DMM and AGF and the experimental data these elevated temperatures suggests a non-negligible contribution from additional modes contributing to TBC that not accounted for in the fundamental assumptions of these harmonic formalisms, such as inelastic scattering. Given the high quality of these ZnO/GaN interface, these results provide an invaluable critical and quantitive assessment of the accuracy of assumptions in the current state of the art of computational approaches for predicting the phonon TBC across interfaces.

cond-mat.mtrl-sci↗

arXiv2017

Adverse effects of polymer coating on heat transport at solid-liquid interface

The ability of metallic nanoparticles to supply heat to a liquid environment under exposure to an external optical field has attracted growing interest for biomedical applications. Controlling the thermal transport properties at a solid-liquid interface then appears to be particularly relevant. In this work, we address the thermal transport between water and a gold surface coated by a polymer layer. Using molecular dynamics simulations, we demonstrate that increasing the polymer density displaces the domain resisting to the heat flow, while it doesn't affect the final amount of thermal energy released in the liquid. This unexpected behavior results from a trade-off established by the increasing polymer density which couples more efficiently with the solid but initiates a counterbalancing resistance with the liquid.

physics.chem-ph↗

arXiv2016

Designing nanostructures for interfacial 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-Pérot 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.

cond-mat.mtrl-sci↗