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Ming Hu

Ming Hu appears in the imported research catalog. Authorship, coauthor and topic links are available while profile ownership is still unclaimed.

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cond-mat.mtrl-sciMachine Learningphysics.comp-phcond-mat.mes-hallmath.OCComputational Engineering, Finance, and ScienceComputer VisionData Structures and AlgorithmsDistributed, Parallel, and Cluster Computingecon.THNeural and Evolutionary Computingphysics.app-ph

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Published work

24 published item(s)

preprint2026arXiv

Constant-Factor Algorithms for Revenue Management with Consecutive Stays

We study network revenue management problems motivated by applications such as railway ticket sales and hotel room bookings. Requests, each requiring a resource for a consecutive stay, arrive sequentially with known arrival probabilities. We investigate two scenarios: the accept-or-reject scenario, where a request can be fulfilled by assigning any available resource; and the BAM-based scenario, which generalizes the former by incorporating customer preferences through the basic attraction model (BAM), allowing the platform to offer an assortment of available resources from which the customer may choose. We develop polynomial-time policies and evaluate their performance using approximation ratios, defined as the ratio between the expected revenue of our policy and that of the optimal online algorithm. When each arrival has a fixed request type (e.g., the interval of the stay is fixed), we establish constant-factor guarantees: a ratio of 1 - 1/e for the accept-or-reject scenario and 0.25 for the BAM-based scenario. We further extend these results to the case where the request type is random (e.g., the interval of the stay is random). In this setting, the approximation ratios incur an additional multiplicative factor of 1 - 1/e, resulting in guarantees of at least 0.399 for the accept-or-reject scenario and 0.156 for the BAM-based scenario. These constant-factor guarantees stand in sharp contrast to the prior nonconstant competitive ratios that are benchmarked against the offline optimum.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

DermAgent: A Self-Reflective Agentic System for Dermatological Image Analysis with Multi-Tool Reasoning and Traceable Decision-Making

Dermatological diagnosis requires integrating fine-grained visual perception with expert clinical knowledge. Although Multimodal Large Language Models (MLLMs) facilitate interactive medical image analysis, their application in dermatology is hindered by insufficient domain-specific grounding and hallucinations. To address these issues, we propose DermAgent, a collaborative multi-tool agent that orchestrates seven specialized vision and language modules within a Plan-Execute-Reflect framework. DermAgent delivers stepwise, traceable diagnostic reasoning through three core components. First, it employs complementary visual perception tools for comprehensive morphological description, dermoscopic concept annotation, and disease diagnosis. Second, to overcome the lack of domain prior, a dual-modality retrieval module anchors every prediction in external evidence by cross-referencing 413,210 diagnosed image cases and 3,199 clinical guideline chunks. To further mitigate hallucinations, a deterministic critic module conducts strict post-hoc auditing via confidence, coverage, and conflict gates, automatically detecting inter-source disagreements to trigger targeted self-correction. Extensive experiments on five dermatology benchmarks demonstrate that DermAgent consistently outperforms state-of-the-art MLLMs and medical agent baselines across zero-shot fine-grained disease diagnosis, concept annotation, and clinical captioning tasks, exceeding GPT-4o by 17.6% in skin disease diagnostic accuracy and 3.15% in captioning ROUGE-L. Our code is available at https://github.com/YizeezLiu/DermAgent.

0 citations0 reviewsNo code linkedOpen access
preprint2025arXiv

A Comprehensive Assessment and Benchmark Study of Large Atomistic Foundation Models for Phonons

The rapid development of universal machine learning potentials (uMLPs) has enabled efficient, accurate predictions of diverse material properties across broad chemical spaces. While their capability for modeling phonon properties is emerging, systematic benchmarking across chemically diverse systems remains limited. We evaluate six recent uMLPs (EquiformerV2, MatterSim, MACE, and CHGNet) on 2,429 crystalline materials from the Open Quantum Materials Database. Models were used to compute atomic forces in displaced supercells, derive interatomic force constants (IFCs), and predict phonon properties including lattice thermal conductivity (LTC), compared with density functional theory (DFT) and experimental data. The EquiformerV2 pretrained model trained on the OMat24 dataset exhibits strong performance in predicting atomic forces and third-order IFC, while its fine-tuned counterpart consistently outperforms other models in predicting second-order IFC, LTC, and other phonon properties. Although MACE and CHGNet demonstrated comparable force prediction accuracy to EquiformerV2, notable discrepancies in IFC fitting led to poor LTC predictions. Conversely, MatterSim, despite lower force accuracy, achieved intermediate IFC predictions, suggesting error cancellation and complex relationships between force accuracy and phonon predictions. This benchmark guides the evaluation and selection of uMLPs for high-throughput screening of materials with targeted thermal transport properties.

0 citations0 reviewsNo code linkedOpen access
preprint2025arXiv

In context learning Foundation models for Materials Property Prediction with Small datasets

Foundation models (FMs) have recently shown remarkable in-context learning (ICL) capabilities across diverse scientific domains. In this work, we introduce a unified in-context learning foundation model (ICL-FM) framework for materials property prediction that integrates both composition-based and structure-aware representations. The proposed approach couples the pretrained TabPFN transformer with graph neural network (GNN)-derived embeddings and our novel MagpieEX descriptors. MagpieEX augments traditional features with cation-anion interaction data to explicitly measure bond ionicity and charge-transfer asymmetry, capturing interatomic bonding characteristics that influence vibrational and thermal transport properties. Comprehensive experiments on the MatBench benchmark suite and a standalone lattice thermal conductivity (LTC) dataset demonstrate that ICL-FM achieves competitive or superior performance to state-of-the-art (SOTA) models with significantly reduced training costs. Remarkably, the training-free ICL-FM outperformed sophisticated SOTA GNN models in five out of six representative composition-based tasks, including a significant 9.93\% improvement in phonon frequency prediction. On the LTC dataset, the FM effectively models complex phenomena such as phonon-phonon scattering and atomic mass contrast. t-SNE analysis reveals that the FM acts as a physics-aware feature refiner, transforming raw, disjoint feature clusters into continuous manifolds with gradual property transitions. This restructured latent space enhances interpolative prediction accuracy while aligning learned representations with underlying physical laws. This study establishes ICL-FM as a generalizable, data-efficient paradigm for materials informatics.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

3D large-scale fused silica microfluidic chips enabled by hybrid laser microfabrication for continuous-flow UV photochemical synthesis

We demonstrate a hybrid laser microfabrication approach, which combines the technical merits of ultrafast laser-assisted chemical etching and carbon dioxide laser-induced in-situ melting, for centimeter-scale and bonding-free fabrication of 3D complex hollow microstructures in fused silica glass. With the developed approach, large-scale fused silica microfluidic chips with integrated 3D cascaded micromixing units can be reliably manufactured. High-performance on-chip mixing and continuous-flow photochemical synthesis under UV LEDs irradiation at ~280 nm were demonstrated using the manufactured chip, indicating a powerful capability for versatile fabrication of highly transparent all-glass microfluidic reactors for on-chip photochemical synthesis.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Electrically driven robust tuning of lattice thermal conductivity

Two-dimensional (2D) materials represented by graphene stand out in future electrical industry and have been widely studied. As a commonly existing factor in electronic devices, the electric field has been extensively utilized to modulate the performance. However, how the electric field regulates thermal transport is rarely studied. Herein, we investigate the modulation of thermal transport properties by applying the external electric field ranging from 0 to 0.4 VA-1, with bilayer graphene, monolayer silicene, and germanene as study cases. The monotonic decreasing trend of thermal conductivity of all the three materials is revealed. The significant effect on the scattering rate is found to be responsible for the decreased thermal conductivity by electric field. Further evidences show that the reconstruction of internal electric field and the generation of induced charges lead to the increased scattering rate from strong phonon anharmonicity. Thus, the ultra-low thermal conductivity emerges with external electric field applied. Applying external electric field to regulate thermal conductivity enlightens the constructive idea for high-efficient thermal management.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

FedCAT: Towards Accurate Federated Learning via Device Concatenation

As a promising distributed machine learning paradigm, Federated Learning (FL) enables all the involved devices to train a global model collaboratively without exposing their local data privacy. However, for non-IID scenarios, the classification accuracy of FL models decreases drastically due to the weight divergence caused by data heterogeneity. Although various FL variants have been studied to improve model accuracy, most of them still suffer from the problem of non-negligible communication and computation overhead. In this paper, we introduce a novel FL approach named Fed-Cat that can achieve high model accuracy based on our proposed device selection strategy and device concatenation-based local training method. Unlike conventional FL methods that aggregate local models trained on individual devices, FedCat periodically aggregates local models after their traversals through a series of logically concatenated devices, which can effectively alleviate the model weight divergence problem. Comprehensive experimental results on four well-known benchmarks show that our approach can significantly improve the model accuracy of state-of-the-art FL methods without causing extra communication overhead.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

FedEntropy: Efficient Device Grouping for Federated Learning Using Maximum Entropy Judgment

Along with the popularity of Artificial Intelligence (AI) and Internet-of-Things (IoT), Federated Learning (FL) has attracted steadily increasing attentions as a promising distributed machine learning paradigm, which enables the training of a central model on for numerous decentralized devices without exposing their privacy. However, due to the biased data distributions on involved devices, FL inherently suffers from low classification accuracy in non-IID scenarios. Although various device grouping method have been proposed to address this problem, most of them neglect both i) distinct data distribution characteristics of heterogeneous devices, and ii) contributions and hazards of local models, which are extremely important in determining the quality of global model aggregation. In this paper, we present an effective FL method named FedEntropy with a novel dynamic device grouping scheme, which makes full use of the above two factors based on our proposed maximum entropy judgement heuristic.Unlike existing FL methods that directly aggregate local models returned from all the selected devices, in one FL round FedEntropy firstly makes a judgement based on the pre-collected soft labels of selected devices and then only aggregates the local models that can maximize the overall entropy of these soft labels. Without collecting local models that are harmful for aggregation, FedEntropy can effectively improve global model accuracy while reducing the overall communication overhead. Comprehensive experimental results on well-known benchmarks show that, FedEntropy not only outperforms state-of-the-art FL methods in terms of model accuracy and communication overhead, but also can be integrated into them to enhance their classification performance.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

FedMR: Fedreated Learning via Model Recombination

As a promising privacy-preserving machine learning method, Federated Learning (FL) enables global model training across clients without compromising their confidential local data. However, existing FL methods suffer from the problem of low inference performance for unevenly distributed data, since most of them rely on Federated Averaging (FedAvg)-based aggregation. By averaging model parameters in a coarse manner, FedAvg eclipses the individual characteristics of local models, which strongly limits the inference capability of FL. Worse still, in each round of FL training, FedAvg dispatches the same initial local models to clients, which can easily result in stuck-at-local-search for optimal global models. To address the above issues, this paper proposes a novel and effective FL paradigm named FedMR (Federating Model Recombination). Unlike conventional FedAvg-based methods, the cloud server of FedMR shuffles each layer of collected local models and recombines them to achieve new models for local training on clients. Due to the fine-grained model recombination and local training in each FL round, FedMR can quickly figure out one globally optimal model for all the clients. Comprehensive experimental results demonstrate that, compared with state-of-the-art FL methods, FedMR can significantly improve the inference accuracy without causing extra communication overhead.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Genetic programming-based learning of carbon interatomic potential for materials discovery

Efficient and accurate interatomic potential functions are critical to computational study of materials while searching for structures with desired properties. Traditionally, potential functions or energy landscapes are designed by experts based on theoretical or heuristic knowledge. Here, we propose a new approach to leverage strongly typed parallel genetic programming (GP) for potential function discovery. We use a multi-objective evolutionary algorithm with NSGA-III selection to optimize individual age, fitness, and complexity through symbolic regression. With a DFT dataset of 863 unique carbon allotrope configurations drawn from 858 carbon structures, the generated potentials are able to predict total energies within $\pm 7.70$ eV at low computational cost while generalizing well across multiple carbon structures. Our code is open source and available at \url{http://www.github.com/usccolumbia/mlpotential

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

GitFL: Adaptive Asynchronous Federated Learning using Version Control

As a promising distributed machine learning paradigm that enables collaborative training without compromising data privacy, Federated Learning (FL) has been increasingly used in AIoT (Artificial Intelligence of Things) design. However, due to the lack of efficient management of straggling devices, existing FL methods greatly suffer from the problems of low inference accuracy and long training time. Things become even worse when taking various uncertain factors (e.g., network delays, performance variances caused by process variation) existing in AIoT scenarios into account. To address this issue, this paper proposes a novel asynchronous FL framework named GitFL, whose implementation is inspired by the famous version control system Git. Unlike traditional FL, the cloud server of GitFL maintains a master model (i.e., the global model) together with a set of branch models indicating the trained local models committed by selected devices, where the master model is updated based on both all the pushed branch models and their version information, and only the branch models after the pull operation are dispatched to devices. By using our proposed Reinforcement Learning (RL)-based device selection mechanism, a pulled branch model with an older version will be more likely to be dispatched to a faster and less frequently selected device for the next round of local training. In this way, GitFL enables both effective control of model staleness and adaptive load balance of versioned models among straggling devices, thus avoiding the performance deterioration. Comprehensive experimental results on well-known models and datasets show that, compared with state-of-the-art asynchronous FL methods, GitFL can achieve up to 2.64X training acceleration and 7.88% inference accuracy improvements in various uncertain scenarios.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Model-Contrastive Learning for Backdoor Defense

Due to the popularity of Artificial Intelligence (AI) techniques, we are witnessing an increasing number of backdoor injection attacks that are designed to maliciously threaten Deep Neural Networks (DNNs) causing misclassification. Although there exist various defense methods that can effectively erase backdoors from DNNs, they greatly suffer from both high Attack Success Rate (ASR) and a non-negligible loss in Benign Accuracy (BA). Inspired by the observation that a backdoored DNN tends to form a new cluster in its feature spaces for poisoned data, in this paper we propose a novel two-stage backdoor defense method, named MCLDef, based on Model-Contrastive Learning (MCL). In the first stage, our approach performs trigger inversion based on trigger synthesis, where the resultant trigger can be used to generate poisoned data. In the second stage, under the guidance of MCL and our defined positive and negative pairs, MCLDef can purify the backdoored model by pulling the feature representations of poisoned data towards those of their clean data counterparts. Due to the shrunken cluster of poisoned data, the backdoor formed by end-to-end supervised learning is eliminated. Comprehensive experimental results show that, with only 5% of clean data, MCLDef significantly outperforms state-of-the-art defense methods by up to 95.79% reduction in ASR, while in most cases the BA degradation can be controlled within less than 2%. Our code is available at https://github.com/WeCanShow/MCL.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Model-Free Assortment Pricing with Transaction Data

We study the problem when a firm sets prices for products based on the transaction data, i.e., which product past customers chose from an assortment and what were the historical prices that they observed. Our approach does not impose a model on the distribution of the customers' valuations and only assumes, instead, that purchase choices satisfy incentive-compatible constraints. The individual valuation of each past customer can then be encoded as a polyhedral set, and our approach maximizes the worst-case revenue assuming that new customers' valuations are drawn from the empirical distribution implied by the collection of such polyhedra. We show that the optimal prices in this setting can be approximated at any arbitrary precision by solving a compact mixed-integer linear program. Moreover, we study the single-product case and relate it to the traditional model-based approach. We also design three approximation strategies that are of low computational complexity and interpretable. Comprehensive numerical studies based on synthetic and real data suggest that our pricing approach is uniquely beneficial when the historical data has a limited size or is susceptible to model misspecification.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

Phonon Scattering in the Complex Strain Field of a Dislocation

Strain engineering is critical to the performance enhancement of electronic and thermoelectric devices because of its influence on the material thermal conductivity. However, current experiments cannot probe the detailed physics of the phonon-strain interaction due to the complex, inhomogeneous, and long-distance features of the strain field in real materials. Dislocations provide us with an excellent model to investigate these inhomogeneous strain fields. In this study, non-equilibrium molecular dynamics simulations were used to study the lattice thermal conductivity of PbTe under different strain status tuned by dislocation densities. The extended 1D McKelvey-Shockley flux method was used to analyze the frequency dependence of phonon scattering in the inhomogeneously strained regions of dislocations. A spatially resolved phonon dislocation scattering process was shown, where the unequal strain in different regions affected the magnitude and frequency-dependence of the scattering rate. Our study not only advances the knowledge of strain scattering of phonon propagation but offers fundamental guidance on optimizing thermal management by structure design.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

Probing the Phonon Mean Free Paths in Dislocation Core by Molecular Dynamics Simulation

Thermal management is extremely important for designing high-performance devices. The lattice thermal conductivity of materials is strongly dependent on the structural defects at different length scales, particularly point defects like vacancies, line defects like dislocations, and planar defects such as grain boundaries. Traditionally, the McKelvey-Shockley phonon Boltzmann's transport equation (BTE) method combined with molecular dynamics simulations has been widely used to evaluate the phonon mean free paths (MFPs) in defective systems. However, this method can only provide the aggregate MFPs of the whole sample. It is, therefore, challenging to extract the MFPs in the different regions with different thermal properties. In this study, the 1D McKelvey-Shockley phonon BTE method was extended to model inhomogeneous materials, where the effect of defects on the phonon MFPs is explicitly obtained. Then, the method was used to study the phonon interactions with the core structure of an edge dislocation. The phonon MFPs in the dislocation core were obtained and consistent with the analytical model such that high frequency phonons are likely to be scattered in this area. This method not only advances the knowledge of phonon-dislocation scattering but also shows the potential to investigate phonon transport behaviors in more complicated materials.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Machine Learning based prediction of noncentrosymmetric crystal materials

Noncentrosymmetric materials play a critical role in many important applications such as laser technology, communication systems,quantum computing, cybersecurity, and etc. However, the experimental discovery of new noncentrosymmetric materials is extremely difficult. Here we present a machine learning model that could predict whether the composition of a potential crystalline structure would be centrosymmetric or not. By evaluating a diverse set of composition features calculated using matminer featurizer package coupled with different machine learning algorithms, we find that Random Forest Classifiers give the best performance for noncentrosymmetric material prediction, reaching an accuracy of 84.8% when evaluated with 10 fold cross-validation on the dataset with 82,506 samples extracted from Materials Project. A random forest model trained with materials with only 3 elements gives even higher accuracy of 86.9%. We apply our ML model to screen potential noncentrosymmetric materials from 2,000,000 hypothetical materials generated by our inverse design engine and report the top 20 candidate noncentrosymmetric materials with 2 to 4 elements and top 20 borate candidates

0 citations0 reviewsNo code linkedOpen access
preprint2019arXiv

Generative adversarial networks (GAN) based efficient sampling of chemical space for inverse design of inorganic materials

A major challenge in materials design is how to efficiently search the vast chemical design space to find the materials with desired properties. One effective strategy is to develop sampling algorithms that can exploit both explicit chemical knowledge and implicit composition rules embodied in the large materials database. Here, we propose a generative machine learning model (MatGAN) based on a generative adversarial network (GAN) for efficient generation of new hypothetical inorganic materials. Trained with materials from the ICSD database, our GAN model can generate hypothetical materials not existing in the training dataset, reaching a novelty of 92.53% when generating 2 million samples. The percentage of chemically valid (charge neutral and electronegativity balanced) samples out of all generated ones reaches 84.5% by our GAN when trained with materials from ICSD even though no such chemical rules are explicitly enforced in our GAN model, indicating its capability to learn implicit chemical composition rules. Our algorithm could be used to speed up inverse design or computational screening of inorganic materials.

0 citations0 reviewsNo code linkedOpen access
preprint2016arXiv

Diverse anisotropy of phonon transport in two-dimensional IV-VI compounds: A comparative study

New classes two-dimensional (2D) materials beyond graphene, including layered and non-layered, and their heterostructures, are currently attracting increasing interest due to their promising applications in nanoelectronics, optoelectronics and clean energy, where thermal transport property is one of the fundamental physical parameters. In this paper, we systematically investigated the phonon transport properties of 2D orthorhombic group IV-VI compounds of $GeS$, $GeSe$, $SnS$ and $SnSe$ by solving the Boltzmann transport equation (BTE) based on first-principles calculations. Despite the similar puckered (hinge-like) structure along the armchair direction as phosphorene, the four monolayer compounds possess diverse anisotropic properties in many aspects, such as phonon group velocity, Young's modulus and lattice thermal conductivity ($κ$), etc. Especially, the $κ$ along the zigzag and armchair directions of monolayer $GeS$ shows the strongest anisotropy while monolayer $SnS$ and $SnSe$ shows an almost isotropy in phonon transport. The origin of the diverse anisotropy is fully studied and the underlying mechanism is discussed in detail. With limited size, the $κ$ could be effectively lowered, and the anisotropy could be effectively modulated by nanostructuring, which would extend the applications in nanoscale thermoelectrics and thermal management. Our study offers fundamental understanding of the anisotropic phonon transport properties of 2D materials, and would be of significance for further study, modulation and aplications in emerging technologies.

0 citations0 reviewsNo code linkedOpen access
preprint2016arXiv

Methodology for determining the electronic thermal conductivity of metals via direct non-equilibrium ab initio molecular dynamics

Many physical properties of metals can be understood in terms of the free electron model, as proven by the Wiedemann-Franz law. According to this model, electronic thermal conductivity ($κ_{el}$) can be inferred from the Boltzmann transport equation (BTE). However, the BTE does not perform well for some complex metals, such as Cu. Moreover, the BTE cannot clearly describe the origin of the thermal energy carried by electrons or how this energy is transported in metals. The charge distribution of conduction electrons in metals is known to reflect the electrostatic potential (EP) of the ion cores. Based on this premise, we develop a new methodology for evaluating $κ_{el}$ by combining the free electron model and non-equilibrium ab initio molecular dynamics (NEAIMD) simulations. We demonstrate that the kinetic energy of thermally excited electrons originates from the energy of the spatial electrostatic potential oscillation (EPO), which is induced by the thermal motion of ion cores. This method directly predicts the $κ_{el}$ of pure metals with a high degree of accuracy.

0 citations0 reviewsNo code linkedOpen access
preprint2016arXiv

On the mechanism of hydrophilicity of graphene

It is generally accepted that the hydrophilic property of graphene can be affected by the underlying substrate. However, the role of intrinsic vs. substrate contributions and the related mechanisms are vividly debated. Here we show that the intrinsic hydrophilicity of graphene can be intimately connected to the position of its Fermi level, which affects the interaction between graphene and water molecules. The underlying substrate, or dopants, can tune hydrophilicity by modulating the Fermi level of graphene. By shifting the Fermi level of graphene away from its Dirac point, via either chemical or electrical voltage doping, we show enhanced hydrophilicity with experiments and first principle simulations. Increased vapor condensation on graphene, induced by a simple shifting of its Fermi level, exemplifies applications in the area of interfacial transport phenomena.

0 citations0 reviewsNo code linkedOpen access
preprint2015arXiv

Anisotropic intrinsic lattice thermal conductivity of phosphorene from first principles

Phosphorene, the single layer counterpart of black phosphorus, is a novel two-dimensional semiconductor with high carrier mobility and a large fundamental direct band gap, which has attracted tremendous interest recently. Its potential applications in nano-electronics and thermoelectrics call for a fundamental study of the phonon transport. Here, we calculate the intrinsic lattice thermal conductivity of phosphorene by solving the phonon Boltzmann transport equation (BTE) based on first-principles calculations. The thermal conductivity of phosphorene at $300\,\mathrm{K}$ is $30.15\,\mathrm{Wm^{-1}K^{-1}}$ (zigzag) and $13.65\,\mathrm{Wm^{-1}K^{-1}}$ (armchair), showing an obvious anisotropy along different directions. The calculated thermal conductivity fits perfectly to the inverse relation with temperature when the temperature is higher than Debye temperature ($Θ_D = 278.66\,\mathrm{K}$). In comparison to graphene, the minor contribution around $5\%$ of the ZA mode is responsible for the low thermal conductivity of phosphorene. In addition, the representative mean free path (MFP), a critical size for phonon transport, is also obtained.

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preprint2015arXiv

Quantitatively Analyzing Phonon Spectral Contribution of Thermal Conductivity Based on Non-Equilibrium Molecular Dynamics Simulation I: From Space Fourier Transform

Probing detailed spectral dependence of phonon transport properties in bulk materials is critical to improve the function and performance of structures and devices in a diverse spectrum of technologies. Currently, such information can only be provided by the phonon spectral energy density (SED) or equivalently time domain normal mode analysis (TDNMA) methods in the framework of equilibrium molecular dynamics simulation (EMD), but has not been realized in non-equilibrium molecular dynamics simulations (NEMD) so far. In this paper we generate a new scheme directly based on NEMD and lattice dynamics theory, called time domain direct decomposition method (TDDDM), to predict the phonon mode specific thermal conductivity. Two benchmark cases of Lennard-Jones (LJ) Argon and Stillinger-Weber (SW) Si are studied by TDDDM to characterize contributions of individual phonon modes to overall thermal conductivity and the results are compared with that predicted using SED and TDNMA. Excellent agreements are found for both cases, which confirm the validity of our TDDDM approach. The biggest advantage of TDDDM is that it can be used to investigate the size effect of individual phonon modes in NEMD simulations, which cannot be tackled by SED and TDNMA in EMD simulations currently. We found that the phonon modes with mean free path larger than the system size are truncated in NEMD and contribute little to the overall thermal conductivity. The TDDDM provides direct physical origin for the well-known strong size effects in thermal conductivity prediction by NEMD.

0 citations0 reviewsNo code linkedOpen access
preprint2015arXiv

Unexpectedly Large Tunability of Lattice Thermal Conductivity of Monolayer Silicene via Mechanical Strain

Strain engineering is one of the most promising and effective routes toward continuously tuning the electronic and optic properties of materials, while thermal properties are generally believed to be insensitive to mechanical strain. In this paper, the strain-dependent thermal conductivity of monolayer silicene under uniform bi-axial tension is computed by solving the phonon Boltzmann transport equation with force constants extracted from first-principles calculations. Unlike the commonly believed understanding that thermal conductivity only slightly decreases with increased tensile strain for bulk materials, it is found that the thermal conductivity of silicene first increases dramatically with strain and then slightly decreases when the applied strain increases further. At a tensile strain of 4%, the highest thermal conductivity is found to be about 7.5 times that of unstrained one. Such an unusual strain dependence is mainly attributed to the dramatic enhancement in the acoustic phonon lifetime. Such enhancement plausibly originates from the flattening of the buckling of the silicene structure upon stretching, which is unique for silicene as compared with other common two-dimensional materials. Our findings offer perspectives of modulating the thermal properties of low-dimensional structures for applications such as thermoelectrics, thermal circuits, and nanoelectronics.

0 citations0 reviewsNo code linkedOpen access
preprint2014arXiv

Mechanics of nanoscale wrinkling of graphene on a non-developable spherical surface

The configuration of graphene (GE) sheet conforming to the spherical surface substrate is studied through theoretical model and molecular simulations. Two basic configurations are observed: fully conformation and wrinkling. The final configuration of the adsorbed GE results from the competition between two energy terms: the adhesion energy between GE and substrate, the strain energy stored in the GE due to the deformations. Here, we derive theoretical solutions by accounting for two energy terms, and predict the final morphology of GE on the spherical surface (a special kind of nano-developable curved surface) substrate with using the phase diagram. A critical cone angle of the absorbed GE for an arbitrary spherical surface substrate is obtained. Fully conformation of GE is observed when the cone angle of absorbed GE is below the critical value, otherwise wrinkles appear. Molecular simulations are implemented to verify the theoretical model with results agree well with theoretical predictions. Results from our present work can offer a guide for designing new functional graphene electronical devices (such as nanoswithes) and fabricating high quality nanostructured coating (Fig. 13).

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