arXiv subjects

Bing Huang

Publications and source records attributed to Bing Huang.

At least 73 records · Page 4Linked to original sources

arXiv2020

Realization of Semiconducting Layered Multiferroic Heterojunctions via Asymmetrical Magnetoelectric Coupling

Two-dimensional (2D) semiconducting multiferroics that can effectively couple magnetic and polarization (P) orders have great interest for both fundamental research and technological applications in nanoscale, which are, however, rare in nature. In this study, we propose a general mechanism to realize semiconducting 2D multiferroics via vdW heterojunction engineering, as demonstrated in a typical heterostructure consisting of magnetic bilayer CrI3 (bi-CrI3) and ferroelectric monolayer In2Se3. Interestingly, the novel indirect orbital coupling between Se 4p and Cr 3d orbitals, intermediated by the interfacial I 5p orbitals, are switchable in the opposite P configurations, resulting in an unexpected mechanism of strong asymmetrical magnetoelectric coupling. Therefore, along with the noticeable ferroelectric energy barrier induced by In2Se3, the realization of opposite magnetic orders in opposite P configurations can eventually result in the novel multiferroicity in bi-CrI3/In2Se3. Finally, we demonstrate that our mechanism can generally be applied to design other vdW multiferroics even with tunable layer thickness.

cond-mat.mtrl-sci↗

arXiv2020

A Configuration Based Pumped Storage Hydro Model in MISO Day-Ahead Market

Pumped storage hydro units (PSHU) can provide flexibility to power systems. This becomes particularly valuable in recent years with the increasing shares of intermittent renewable resources. However, due to emphasis on thermal generation in the current market practices, the flexibility from PSHUs have not been fully explored and utilized. This paper proposes a configuration based pumped storage hydro (PSH) model for the day-ahead market, in order to enhance the use of PSH resources in the system. A strategic design of incorporating and fully optimizing PSHUs in the day-ahead market is presented. We show the compactness of the proposed model. Numerical studies are presented in an illustrative test system and the Midcontinent Independent System Operator (MISO) system.

eess.SY↗

arXiv2020

Quantum machine learning using atom-in-molecule-based fragments selected on-the-fly

First principles based exploration of chemical space deepens our understanding of chemistry, and might help with the design of new materials or experiments. Due to the computational cost of quantum chemistry methods and the immens number of theoretically possible stable compounds comprehensive in-silico screening remains prohibitive. To overcome this challenge, we combine atoms-in-molecules based fragments, dubbed "amons" (A), with active learning in transferable quantum machine learning (ML) models. The efficiency, accuracy, scalability, and transferability of resulting AML models is demonstrated for important molecular quantum properties, such as energies, forces, atomic charges NMR shifts, polarizabilities, and for systems ranging from organic molecules over 2D materials and water clusters to Watson-Crick DNA base-pairs and even ubiquitin. Conceptually, the AML approach extends Mendeleev's table to effectively account for chemical environments, which allows the systematic reconstruction of many chemistries from local building blocks.

physics.chem-ph↗

arXiv2020

Dictionary of 140k GDB and ZINC derived AMONs

We present all {\bf A}mons for {\bf G}DB and {\bf Z}inc data-bases using no more than 7 non-hydrogen atoms (AGZ7)---a calculated organic chemistry building-block dictionary based on the AMON approach [Huang and von Lilienfeld, {\em Nature Chemistry} (2020)]. AGZ7 records Cartesian coordinates of compositional and constitutional isomers, as well as properties for $\sim$140k small organic molecules obtained by systematically fragmenting all molecules of Zinc and the majority of GDB17 into smaller entities, saturating with hydrogens, and containing no more than 7 heavy atoms (excluding hydrogen atoms). AGZ7 cover the elements \{H, B, C, N, O, F, Si, P, S, Cl, Br, Sn and I\} and includes optimized geometries, total energy and its decomposition, Mulliken atomic charges, dipole moment vectors, quadrupole tensors, electronic spatial extent, eigenvalues of all occupied orbitals, LUMO, gap, isotropic polarizability, harmonic frequencies, reduced masses, force constants, IR intensity, normal coordinates, rotational constants, zero-point energy, internal energy, enthalpy, entropy, free energy, and heat capacity (all at ambient conditions) using B3LYP/cc-pVTZ (pseudopotentials were used for Sn and I) level of theory. We exemplify the usefulness of this data set with AMON based machine learning models of total potential energy predictions of seven of the most rigid GDB-17 molecules.

physics.chem-ph↗

arXiv2020

Dueling Deep Q Network for Highway Decision Making in Autonomous Vehicles: A Case Study

This work optimizes the highway decision making strategy of autonomous vehicles by using deep reinforcement learning (DRL). First, the highway driving environment is built, wherein the ego vehicle, surrounding vehicles, and road lanes are included. Then, the overtaking decision-making problem of the automated vehicle is formulated as an optimal control problem. Then relevant control actions, state variables, and optimization objectives are elaborated. Finally, the deep Q-network is applied to derive the intelligent driving policies for the ego vehicle. Simulation results reveal that the ego vehicle could safely and efficiently accomplish the driving task after learning and training.

cs.AI↗

arXiv2020

Decision-making Strategy on Highway for Autonomous Vehicles using Deep Reinforcement Learning

Autonomous driving is a promising technology to reduce traffic accidents and improve driving efficiency. In this work, a deep reinforcement learning (DRL)-enabled decision-making policy is constructed for autonomous vehicles to address the overtaking behaviors on the highway. First, a highway driving environment is founded, wherein the ego vehicle aims to pass through the surrounding vehicles with an efficient and safe maneuver. A hierarchical control framework is presented to control these vehicles, which indicates the upper-level manages the driving decisions, and the lower-level cares about the supervision of vehicle speed and acceleration. Then, the particular DRL method named dueling deep Q-network (DDQN) algorithm is applied to derive the highway decision-making strategy. The exhaustive calculative procedures of deep Q-network and DDQN algorithms are discussed and compared. Finally, a series of estimation simulation experiments are conducted to evaluate the effectiveness of the proposed highway decision-making policy. The advantages of the proposed framework in convergence rate and control performance are illuminated. Simulation results reveal that the DDQN-based overtaking policy could accomplish highway driving tasks efficiently and safely.

eess.SP↗

arXiv2020

Service mining for Internet of Things

A service mining framework is proposed that enables discovering interesting relationships in Internet of Things services bottom-up. The service relationships are modeled based on spatial-temporal aspects, environment, people, and operation. An ontology-based service model is proposed to describe services. We present a set of metrics to evaluate the interestingness of discovered service relationships. Analytical and simulation results are presented to show the effectiveness of the proposed evaluation measures.

cs.NI↗

arXiv2020

Enabling Edge Cloud Intelligence for Activity Learning in Smart Home

We propose a novel activity learning framework based on Edge Cloud architecture for the purpose of recognizing and predicting human activities. Although activity recognition has been vastly studied by many researchers, the temporal features that constitute an activity, which can provide useful insights for activity models, have not been exploited to their full potentials by mining algorithms. In this paper, we utilize temporal features for activity recognition and prediction in a single smart home setting. We discover activity patterns and temporal relations such as the order of activities from real data to develop a prompting system. Analysis of real data collected from smart homes was used to validate the proposed method.

cs.AI↗

arXiv2020

Cognitive Amplifier for Internet of Things

We present a Cognitive Amplifier framework to augment things part of an IoT, with cognitive capabilities for the purpose of improving life convenience. Specifically, the Cognitive Amplifier consists of knowledge discovery and prediction components. The knowledge discovery component focuses on finding natural activity patterns considering their regularity, variations, and transitions in real life setting. The prediction component takes the discovered knowledge as the base for inferring what, when, and where the next activity will happen. Experimental results on real-life data validate the feasibility and applicability of the proposed approach.

cs.AI↗

arXiv2019

Realization of Lieb Lattice in Covalent-organic Frameworks with Tunable Topology and Magnetism

Lieb lattice, a two-dimensional edge-depleted square lattice, has been predicted to host various exotic electronic properties due to its unusual band structure, i.e., Dirac cone intersected by a flat band (Dirac-flat bands). Until now, although a few artificial Lieb lattices have been discovered in experiments, the realization of a Lieb lattice in a real material is still unachievable. In this article, based on tight-binding modeling and first-principles calculations, we predict that the two covalent organic frameworks (COFs), i.e., sp2C-COF and sp2N-COF, which have been synthesized in the recent experiments, are actually the first two material realizations of organic-ligand-based Lieb lattice. It is found that the lattice distortion can govern the bandwidth of the Dirac-flat bands and in turn determine its electronic instability against spontaneous spin-polarization during carrier doping. The spin-orbit coupling effects could drive these Dirac-flat bands in a distorted Lieb lattice presenting nontrivial topological properties, which depend on the position of Fermi level. Interestingly, as the hole doping concentration increases, the sp2C-COF can experience the phase transitions from a paramagnetic state to a ferromagnetic one and then to a Néel antiferromagnetic one. Our findings not only confirm the first material realization of Lieb lattice in COFs, but also offer a possible way to achieve tunable topology and magnetism in d- (f-) orbital-free organic lattices.

cond-mat.mtrl-sci↗

arXiv2019

Giant Enhancement of Solid Solubility in Monolayer BNC Alloys by Selective Orbital Coupling

Solid solubility (SS) is one of the most important features of alloys, which is usually difficult to be largely tuned in the entire alloy concentrations by external approaches. Some alloys that were supposed to have promising physical properties could turn out to be much less useful because of their poor SS, e.g., the case for monolayer BNC [(BN)1-x(C2)x] alloys. Until now, an effective approach on significantly enhancing SS of (BN)1-x(C2)x in the entire x is still lacking. In this article, a novel mechanism of selective orbital coupling between high energy wrong-bond states and surface states mediated by the specific substrate has been proposed to stabilize the wrong-bonds and in turn significantly enhance the SS of (BN)1-x(C2)x alloys. Surprisingly, we demonstrate that five ordered alloys, exhibiting variable direct quasi-particle bandgaps from 1.35 to 3.99 eV, can spontaneously be formed at different x when (BN)1-x(C2)x is grown on hcp-phase Cr. Interestingly, the optical transitions around the band edges in these ordered alloys, accompanied by largely tunable exciton binding energies of ~1 eV at different x, are significantly strong due to their unique band structures. Importantly, the disordered (BN)1-x(C2)x alloys, exhibiting fully tunable bandgaps from 0 to ~6 eV in the entire x, can be formed on Cr substrate at the miscibility temperature of ~1200 K, which is greatly reduced compared to that of 4500~5600 K in free-standing form or on other substrates. Our discovery not only may resolve the long-standing SS problem of BNC alloys, but also could significantly extend the applications of BNC alloys for various optoelectronic applications.

cond-mat.mtrl-sci↗

arXiv2019

Enhanced Ramping Product Design to Improve Power System Reliability

The roll-out of a flexible ramping product provides Independent System Operators (ISOs) with the ability to address ramping capacity shortages. ISOs procure flexible ramping capability by committing more generating units or reserving a certain amount of head room of committed units. In this paper, we raise the concern of the possibility that the procured ramping capability cannot be deployed in real-time operations. As a solution to the non-delivery issue, we provide a new ramping product designed to improve reliability and reduce the expected operating cost. The trajectory of start-up and shutdown processes is also considered in determining the ramping capability. A new optimization problem is formulated using mixed integer linear programming to be readily applied to practical power system operation. The performance of this proposed method is verified through simulations using a small-scale system and IEEE 118-bus systems. The simulation results demonstrate that the proposed formulation can realize improved generation scheduling alleviating capacity shortages.

math.OC↗

arXiv2019

Anomalous Dirac Plasmons in 1D Topological Electrides

Plasmon opens up the possibility to efficiently couple light and matter at sub-wavelength scales. In general, the plasmon frequency is dependent of carrier density. This dependency, however, renders fundamentally a weak plasmon intensity at low frequency, especially for Dirac plasmon (DP) widely studied in graphene. Here we demonstrate a new type of DP, excited by a Dirac nodal-surface state, which exhibits an anomalously density-independent frequency. Remarkably, we predict realization of anomalous DP (ADP) in 1D topological electrides, such as Ba3CrN3 and Sr3CrN3, by first-principles calculations. The ADPs in both systems have a density-independent frequency and high intensity, and their frequency can be tuned from terahertz to mid-infrared by changing the excitation direction. Furthermore, the intrinsic weak electron-phonon coupling of anionic electrons in electrides affords an added advantage of ultra-low phonon-assisted damping and hence a long lifetime of the ADPs. Our work paves the way to developing novel plasmonic and optoelectronic devices by combining topological physics with electride materials.

cond-mat.mtrl-sci↗

arXiv2019

An Accurate and Transferable Machine-Learning Interatomic Potential for Silicon

The development of modern ab initio methods has rapidly increased our understanding of physics, chemistry and materials science. Unfortunately, intensive ab initio calculations are intractable for large and complex systems. On the other hand, empirical force fields are less accurate with poor transferability even though they are efficient to handle large and complex systems. The recent development of machine-learning based neural-network (NN) for local atomic environment representation of density functional theory (DFT) has offered a promising solution to this long-standing challenge. Si is one of the most important elements in science and technology, however, an accurate and transferable interatomic potential for Si is still lacking. Here, we develop a generalized NN potential for Si, which correctly predicts the Si(111)-(7x7) ground-state surface reconstruction for the first time and accurately reproduces the DFT results in a wide range of complex Si structures. We envision similar developments will be made for a wide range of materials systems in the near future.

cond-mat.mtrl-sci↗

arXiv2018

Pseudo Dirac Nodal Sphere: Unusual Electronic Structure and Material Realization

Topological semimetals (TSMs) in which conduction and valence bands cross at zero-dimensional (0D) Dirac nodal points (DNPs) or 1D Dirac nodal lines (DNLs), in 3D momentum space, have recently drawn much attention due to their exotic electronic properties. Here we generalize the TSM state further to a higher-symmetry and higher-dimensional pseudo Dirac nodal sphere (PDNS) state, with the band crossings forming a 2D closed sphere at the Fermi level. The PDNS state is characterized with a spherical backbone consisting of multiple crossing DNLs while band degeneracy in between the DNLs is approximately maintained by weak interactions. It exhibits some unique electronic properties and low-energy excitations, such as collective plasmons different from DNPs and DNLs. Based on crystalline symmetries, we theoretically demonstrate two possible types of PDNS states, and identify all the possible band crossings with pairs of 1D irreducible representations to form the PDNS states in 32 point groups. Importantly, we discover that strained MH3 (M= Y, Ho, Tb, Nd) and Si3N2 are materials candidates to realize these two types of PDNS states, respectively. As a high-symmetry-required state, the PDNS semimetal can be regarded as the "parent phase" for other topological gapped and gapless states.

cond-mat.mes-hall↗

arXiv2018

Boosting quantum machine learning models with multi-level combination technique: Pople diagrams revisited

Inspired by Pople diagrams popular in quantum chemistry, we introduce a hierarchical scheme, based on the multi-level combination (C) technique, to combine various levels of approximations made when calculating molecular energies within quantum chemistry. When combined with quantum machine learning (QML) models, the resulting CQML model is a generalized unified recursive kernel ridge regression which exploits correlations implicitly encoded in training data comprised of multiple levels in multiple dimensions. Here, we have investigated up to three dimensions: Chemical space, basis set, and electron correlation treatment. Numerical results have been obtained for atomization energies of a set of $\sim$7'000 organic molecules with up to 7 atoms (not counting hydrogens) containing CHONFClS, as well as for $\sim$6'000 constitutional isomers of C$_7$H$_{10}$O$_2$. CQML learning curves for atomization energies suggest a dramatic reduction in necessary training samples calculated with the most accurate and costly method. In order to generate milli-second estimates of CCSD(T)/cc-pvdz atomization energies with prediction errors reaching chemical accuracy ($\sim$1 kcal/mol), the CQML model requires only $\sim$100 training instances at CCSD(T)/cc-pvdz level, rather than thousands within conventional QML, while more training molecules are required at lower levels. Our results suggest a possibly favourable trade-off between various hierarchical approximations whose computational cost scales differently with electron number.

physics.chem-ph↗

arXiv2018

The fundamentals of quantum machine learning

Within the past few years, we have witnessed the rising of quantum machine learning (QML) models which infer electronic properties of molecules and materials, rather than solving approximations to the electronic Schrodinger equation. The increasing availability of large quantum mechanics reference data sets have enabled these developments. We review the basic theories and key ingredients of popular QML models such as choice of regressor, data of varying trustworthiness, the role of the representation, and the effect of training set selection. Throughout we emphasize the indispensable role of learning curves when it comes to the comparative assessment of different QML models.

physics.chem-ph↗

arXiv2018

Ubiquitous Ideal Spin-Orbit Coupling in a Screw Dislocation in Semiconductors

We theoretically demonstrate that screw dislocation (SD), a 1D topological defect widely present in semiconductors, exhibits ubiquitously a new form of spin-orbit coupling (SOC) effect. Differing from the widely known conventional 2D Rashba-Dresselhaus (RD) SOC effect that typically exists at surfaces/interfaces, the deep-level nature of SD-SOC states in semiconductors readily makes it an ideal SOC. Remarkably, the spin texture of 1D SD-SOC, pertaining to the inherent symmetry of SD, exhibits a significantly higher degree of spin coherency than the 2D RD-SOC. Moreover, the 1D SD-SOC can be tuned by ionicity in compound semiconductors to ideally suppress spin relaxation, as demonstrated by comparative first-principles calculations of SDs in Si/Ge, GaAs, and SiC. Our findings therefore open a new door to manipulating spin transport in semiconductors by taking advantage of an otherwise detrimental topological defect.

cond-mat.mtrl-sci↗