arXiv subjects

Ming Hu

Publications and source records attributed to Ming Hu.

At least 163 records · Page 9Linked to original sources

arXiv2022

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.

cs.LG↗

arXiv2022

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.

physics.comp-ph↗

arXiv2021

Predicting Lattice Phonon Vibrational Frequencies Using Deep Graph Neural Networks

Lattice vibration frequencies are related to many important materials properties such as thermal and electrical conductivity as well as superconductivity. However, computational calculation of vibration frequencies using density functional theory (DFT) methods is too computationally demanding for a large number of samples in materials screening. Here we propose a deep graph neural network-based algorithm for predicting crystal vibration frequencies from crystal structures with high accuracy. Our algorithm addresses the variable dimension of vibration frequency spectrum using the zero padding scheme. Benchmark studies on two data sets with 15,000 and 35,552 samples show that the aggregated $R^2$ scores of the prediction reaches 0.554 and 0.724 respectively. Our work demonstrates the capability of deep graph neural networks to learn to predict phonon spectrum properties of crystal structures in addition to phonon density of states (DOS) and electronic DOS in which the output dimension is constant.

cond-mat.mtrl-sci↗

arXiv2021

High-throughput discovery of novel cubic crystal materials using deep generative neural networks

High-throughput screening has become one of the major strategies for the discovery of novel functional materials. However, its effectiveness is severely limited by the lack of quantity and diversity of known materials deposited in the current materials repositories such as ICSD and OQMD. Recent progress in machine learning and especially deep learning have enabled a generative strategy that learns implicit chemical rules for creating chemically valid hypothetical materials with new compositions and structures. However, current materials generative models have difficulty in generating structurally diverse, chemically valid, and stable materials. Here we propose CubicGAN, a generative adversarial network (GAN) based deep neural network model for large scale generation of novel cubic crystal structures. When trained on 375,749 ternary crystal materials from the OQMD database, we show that our model is able to not only rediscover most of the currently known cubic materials but also generate hypothetical materials of new structure prototypes. A total of 506 such new materials (all of them are either ternary or quarternary) have been verified by DFT based phonon dispersion stability check, several of which have been found to potentially have exceptional functional properties. Considering the importance of cubic materials in wide applications such as solar cells and lithium batteries, our GAN model provides a promising approach to significantly expand the current repository of materials, enabling the discovery of new functional materials via screening. The new crystal structures finally verified by DFT are freely accessible at our Carolina Materials Database http://www.carolinamatdb.org.

cond-mat.mtrl-sci↗

arXiv2021

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.

cond-mat.mtrl-sci↗

arXiv2021

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.

cond-mat.mtrl-sci↗

arXiv2020

Lattice Thermal Conductivity Prediction using Symbolic Regression and Machine Learning

Prediction models of lattice thermal conductivity have wide applications in the discovery of thermoelectrics, thermal barrier coatings, and thermal management of semiconductors. kL is notoriously difficult to predict. While classic models such as the Debye-Callaway model and the Slack model have been used to approximate the kL of inorganic compounds, their accuracy is far from being satisfactory. Herein, we propose a genetic programming based Symbolic Regression approach for explicit kL models and compare it with Multi-Layer Perceptron neural networks and a Random Forest Regressor using a hybrid cross-validation approach including both K-Fold CV and holdout validation. Four formulae have been discovered by our symbolic regression approach that outperform the Slack formula as evaluated on our dataset. Through the analysis of our models' performance and the formulae generated, we found that the trained formulae successfully reproduce the correct physical law that governs the lattice thermal conductivity of materials. We also identified that extrapolation prediction remains to be a key issue in both symbolic regression and regular machine learning methods and find the distribution of the samples place a key role in training a prediction model with high generalization capability.

cond-mat.mtrl-sci↗

arXiv2020

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

physics.comp-ph↗

arXiv2020

Predicting Elastic Properties of Materials from Electronic Charge Density Using 3D Deep Convolutional Neural Networks

Materials representation plays a key role in machine learning based prediction of materials properties and new materials discovery. Currently both graph and 3D voxel representation methods are based on the heterogeneous elements of the crystal structures. Here, we propose to use electronic charge density (ECD) as a generic unified 3D descriptor for materials property prediction with the advantage of possessing close relation with the physical and chemical properties of materials. We developed an ECD based 3D convolutional neural networks (CNNs) for predicting elastic properties of materials, in which CNNs can learn effective hierarchical features with multiple convolving and pooling operations. Extensive benchmark experiments over 2,170 Fm-3m face-centered-cubic (FCC) materials show that our ECD based CNNs can achieve good performance for elasticity prediction. Especially, our CNN models based on the fusion of elemental Magpie features and ECD descriptors achieved the best 5-fold cross-validation performance. More importantly, we showed that our ECD based CNN models can achieve significantly better extrapolation performance when evaluated over non-redundant datasets where there are few neighbor training samples around test samples. As additional validation, we evaluated the predictive performance of our models on 329 materials of space group Fm-3m by comparing to DFT calculated values, which shows better prediction power of our model for bulk modulus than shear modulus. Due to the unified representation power of ECD, it is expected that our ECD based CNN approach can also be applied to predict other physical and chemical properties of crystalline materials.

cond-mat.mtrl-sci↗

arXiv2019

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.

cs.LG↗

arXiv2019

Strong phonon localization in PbTe with dislocations and large deviation to Matthiessen's rule

Dislocations can greatly enhance the figure of merit of thermoelectric materials by prominently reducing thermal conductivity. However, the evolution of phonon modes with different energies when they propagate through a single dislocation is unknown. Here we perform non-equilibrium molecular dynamics simulation to study phonon transport in PbTe crystal with dislocations by excluding boundary scattering and strain coupling effect. The frequency-dependent heat flux, phonon mode analysis, and frequency-dependent phonon mean free paths (MFPs) are presented. The thermal conductivity of PbTe with dislocation density on the order of 10^15m^-2 is decreased by 62%. We provide solid evidence of strong localization of phonon modes in dislocation sample. Moreover, by comparing the frequency-dependent phonon MFPs between atomistic modeling and traditional theory, it is found that the conventional theories are inadequate to describe the phonon behavior throughout the full phonon spectrum, and large deviation to the well-known semi-classical Matthiessen's rule is observed. These results provide insightful guidance for the development of PbTe based thermoelectrics and shed light on new routes for enhancing the performance of existing thermoelectrics by incorporating dislocations.

physics.app-ph↗

arXiv2019

Anomalous thermal transport behavior in graphene-like carbon nitride (C$_3$N)

New classes 2D carbon-based materials beyond graphene have been intensively studied for their promising applications in nano-/opto-/spin-electronics, catalysis, sensors, clean energy, etc. Very recently, the controllable large-scale synthesis of 2D single crystalline carbon nitride (C3N) was reported, which is the first and the only crystalline, hole-free, single-layer carbon nitride with fascinating properties. Herein, we perform a comparative study of thermal transport between monolayer C3N and the parent graphene. The thermal conductivity (k) of C3N shows an anomalous temperature dependence, which is totally different from that for common crystalline materials and deviates largely from the well-known k~1/T relationship. Moreover, the k of C3N is found in surprise to be enlarged by applying bilateral tensile strain, despite its similar planar honeycomb structure as graphene, whose k is reduced upon stretching. The underlying mechanism is revealed by providing direct evidence for the interaction between lone-pair N-s electrons and bonding electrons from C atoms in C3N based on the analysis of orbital-projected electronic structures and electron localization function (ELF). Our study not only make a comprehensive investigation of the thermal transport in graphene-like C3N, but also reveals the physical origins for its anomalous properties, which deepens the understanding of phonon transport in 2D materials.

cond-mat.mtrl-sci↗

arXiv2019

Activated lone-pair electrons lead to low lattice thermal conductivity: a case study of boron arsenide

Reducing thermal conductivity ($κ$) is an efficient way to boost the thermoelectric performance to achieve direct solid-state conversion to electrical power from thermal energy, which has lots of valuable applications in reusing waste resources. In this study, we propose an effective approach for realizing low $κ$ by introducing lone-pair electrons or making the lone-pair electrons stereochemically active through bond nanodesigning. As a case study, by cutting at the (111) cross section of the three-dimensional cubic boron arsenide (c-BAs), the $κ$ is lowered by more than one order of magnitude in the resultant two-dimensional system of graphene-like BAs (g-BAs) due to the stereochemically activated lone-pair electrons. Similar concept can be also extended to other systems with lone-pair electrons beyond BAs, such as group III-V compounds, where a strong correlation between $κ$ modulation and electronegativity difference for binary compounds is found. Thus, the lone-pair electrons combined with a small electronegativity difference could be the indicator of lowering $κ$ through bond nanodesigning to change the coordination environment. The proposed approach for realizing low $κ$ and the underlying mechanism uncovered in this study would largely benefit the design of thermoelectric devices with improved performance, especially in future researches involving novel materials for energy applications.

cond-mat.mtrl-sci↗

arXiv2019

Exploring T-carbon for Energy Applications

Seeking for next-generation energy sources that are economic, sustainable (renewable), clean (environment-friendly), and abundant in earth is crucial when facing the challenges of energy crisis. There have been numerous studies exploring the possibility of carbon based materials to be utilized in future energy applications. In this paper, we introduce T-carbon, which is a theoretically predicted but recently experimentally synthesized carbon allotrope, as a promising material for next-generation energy applications. It is shown that T-carbon can be potentially used in thermoelectrics, hydrogen storage, lithium ion batteries, \emph{etc}. The challenges, opportunities, and possible directions for future studies of energy applications of T-carbon are also addressed. With the development of more environment-friendly technologies, the promising applications of T-carbon in energy fields would not only produce scientifically significant impact in related fields but also lead to a number of industrial and technical applications.

cond-mat.mtrl-sci↗

arXiv2019

A nonlinear hyperelasticity model for single layer blue phosphorus based on ab-initio calculations

A new hyperelastic membrane material model is proposed for single layer blue phosphorus ($β\text{-P}$), also known as blue phosphorene. The model is fully nonlinear and captures the anisotropy of $β\text{-P}$ at large strains. The material model is calibrated from density functional theory (DFT) calculations considering a set of elementary deformation states. Those are pure dilatation and uniaxial stretching along the armchair and zigzag directions. The material model is compared and validated with additional DFT results and existing DFT results from the literature, and the comparison shows good agreement. The new material model can be directly used within computational shell formulations that are for example based on rotation-free isogeometric finite elements. This is demonstrated by simulations of the indentation and vibration of single layer blue phosphorus sheets at micrometer scales. The elasticity constants at small deformations are also reported.

cond-mat.mtrl-sci↗

arXiv2018

Dynamic Type Matching

We consider an intermediary's problem of dynamically matching demand and supply of heterogeneous types in a periodic-review fashion. More specifically, there are two disjoint sets of demand and supply types, and a reward associated with each possible matching of a demand type and a supply type. In each period, demand and supply of various types arrive in random quantities. The platform's problem is to decide on the optimal matching policy to maximize the total discounted rewards minus costs, given that unmatched demand and supply will incur waiting or holding costs, and will be carried over to the next period (with abandonment). For this dynamic matching problem, we provide sufficient conditions on matching rewards such that the optimal matching policy follows a priority hierarchy among possible matching pairs. We show those conditions are satisfied by vertically and unidirectionally horizontally differentiated types, for which quality and distance determine priority, respectively. As a result of the priority property, the optimal matching policy boils down to a match-down-to threshold structure when considering a specific pair of demand and supply types in the priority hierarchy.

math.OC↗

arXiv2018

Insight of the thermal conductivity of $ε-$iron at Earth's core conditions from the newly developed direct $ab~initio$ methodology

The electronic thermal conductivity of iron at Earth's core conditions is an extremely important physical property in geophysics field. However, the exact value of electronic thermal conductivity of iron under extreme pressure and temperature still remains poorly known both experimentally and theoretically. A few recent experimental studies measured the value of the electronic thermal conductivity directly and some theoretical works have predicted the electronic thermal conductivity of iron at Earth's core conditions based on the Kubo-Greenwood method. However, these results differ largely from each other. A very recent research has confirmed that for iron at Earth's core conditions the strength of electron-electron scattering could be comparable to that for electron-phonon scattering, meaning that the electron-electron scattering should also be considered when evaluating the electronic thermal conductivity in the Earth's core situations. Here, by utilizing a newly developed methodology based on direct non-equilibrium $ab~initio$ molecular dynamics simulation coupled with the concept of electrostatic potential oscillation, we predict the electronic thermal conductivity of iron in h.c.p phase. Our methodology inherently includes the electron-phonon and electron-electron interactions under extreme conditions. Our results are comparable to the previous theoretical and experimental studies. More importantly, our methodology provides a new physical picture to describe the heat transfer process in $ε-$iron at Earth's core conditions from the electrostatic potential oscillation point of view and offers a new approach to study thermal transport property of pure metals in planet's cores with different temperature and pressures.

cond-mat.mtrl-sci↗

arXiv2018

The role of resonant bonding in governing the thermal transport properties of two-dimensional black phosphorus

Fundamental insight into lattice dynamics and phonon transport is critical to the efficient manipulation of heat flow, which is one of the appealing thermophysical problems with enormous practical implications. Phosphorene, a novel elemental two-dimensional (2D) semiconductor with high carrier mobility and intrinsically large direct band gap, possesses fascinating chemical and physical properties distinctively different from other 2D materials. The rapidly growing applications of phosphorene in nano-/opto-electronics and thermoelectrics call for fundamental understanding of the thermal transport properties. In this study, based on the analysis of electronic structure and lattice dynamics, we demonstrate the formation of resonant bonding in phosphorene. Fundamental insight into the thermal transport in phosphorene is provided by discussing the role of resonant bonding in driving long-range interactions and strong phonon anharmonicity. We reveal that the strong phonon anharmonicity is associated with the soft transverse optical (TO) phonon modes and arises from the long-range interactions driven by the orbital governed resonant bonding. Our study highlights the physical origin of the phonon anharmonicity in phosphorene, and also provides new insights into phonon transport from the view of orbital states, which would be of great significance to the design and development of high-performance phosphorene based nano-devices.

cond-mat.mtrl-sci↗