arXiv subjects

Shuanglong Liu

Publications and source records attributed to Shuanglong Liu.

At least 19 recordsLinked to original sources

arXiv2026

Using near-flat-band electrons for read-out of molecular spin qubit entangled states

While molecular spin qubits (MSQs) are a promising platform for quantum computing, read-out has been largely limited to electron paramagnetic resonance which is often slow and requires a global system drive. Moreover, because one prerequisite for the Elzerman and Pauli spin blockade readout mechanisms typical of semiconductor spin qubits is tunneling of electrons between sites, these read-out modalities are unavailable in MSQs. Here, we theoretically demonstrate electrical read-out of entangled MSQs via driven many-electron spin unpolarized currents. In particular, using a time-dependent density matrix renormalization group approach we simulate a maximally entangled MSQ pair between two electronic leads. Driving itinerant electrons between the two leads, we find that the conductance is greater when the MSQs are in the entangled singlet state as compared to the entangled triplet state. This contrast in conductance is enhanced when the electronic density of states at the Fermi energy is large and for narrow bandwidth. Our results are readily applicable to molecules supramolecularly functionalizing semiconductors with relatively flat bands such as single-wall carbon nanotubes under a magnetic field.

cond-mat.mes-hall↗

arXiv2025

Floquet Engineering Clock Transitions in Magnetic Molecules

We theoretically study Floquet engineering of magnetic molecules via a time-periodic magnetic field that couples to the emergent total electronic spin of the metal center. By focusing on the low-lying energy levels using an $S = 1$ spin Hamiltonian containing the zero-field and Zeeman terms, we demonstrate their continuous tunability under the Floquet field. Remarkably, under the action of linearly polarized Floquet controls, the energy levels of a clock transition qubit retain their stability against variations in an external static magnetic field. This property is closely linked to having a net-zero total Zeeman shift, which results from both static and effective dynamical contributions. Further, using second-order Van Vleck degenerate perturbation theory, we derived analytically an effective Hamiltonian, which explicitly shows the dependence of the renormalized zero-field tensor on the driving field. Based on our theoretical predictions, experimentalists will be able to dynamically tune qubit energy gaps to values that are useful in their specific laboratory settings, while retaining the spin decoherence suppressing effect of maintaining a clock transition.

quant-ph↗

arXiv2025

Dominant spin-spin relaxation mechanism at clock transition of the $Ho_{x}Y_{1-x}W_{10}$ complex at different concentrations

Spin decoherence poses a significant challenge in molecular magnets, with the nuclear spin bath serving as a prominent source. Intriguingly, spin qubits at the clock transition exhibit remarkable insensitivity to the surrounding nuclear spins. Recent experimental studies have unveiled a correlation between the decoherence time and the density of spin qubits, prompting our investigation into the contribution of the qubit bath to spin decoherence. In this paper, we present a comprehensive theoretical analysis of a few S=1 spin qubits, focusing on their interaction at the clock transition. Employing the exact diagonalization and the cluster correlation expansion (CCE) method, we simulate the dynamics of spin decoherence while varying the density of the qubit bath. To ensure the realism of our simulations, we incorporate structural and energetic parameters derived from previous studies on the HoW10 crystal. Our findings indicate that when the energy mismatch between the energy splittings of two qubits exceeds their interaction strength, they can become effectively insensitive to each other, offering an explanation for the absence of observed changes in the T2 time during experiments with lower qubit densities. Understanding the role of qubit bath density in spin decoherence at the clock transition not only advances our knowledge of decoherence mechanisms but also provides insights for the development of strategies to protect coherence in molecular magnets and other quantum systems. By optimizing the density of spin qubits, we can enhance the coherence properties and pave the way for improved performance of quantum devices. Overall, this study offers valuable insights into the relationship between qubit bath density and spin decoherence at the clock transition, contributing to the broader understanding and control of quantum systems in molecular magnets.

quant-ph↗

arXiv2025

Accurate crystal field Hamiltonians of single-ion magnets at mean-field cost

The effective crystal field Hamiltonian provides the key description of the electronic properties of single-ion magnets, but obtaining its parameters from ab initio computation is challenging. We introduce a simple approach to derive the effective crystal field Hamiltonian through density functional calculations of randomly rotated mean-field states within the low-energy manifold. In benchmarks on five lanthanide-based complexes, we find that we compute with mean-field cost an effective crystal field Hamiltonian that matches the state-of-the-art from much more expensive multi-configurational quantum chemistry methods. In addition, we are able to reproduce the experimental low-energy spectrum and magnetic properties with an accuracy exceeding prior attempts. Due to its low cost, our approach provides a crucial ingredient in the computational design of single-ion magnets with tailored physical properties and low-energy spectra.

physics.chem-ph↗

arXiv2025

Tuning the Magnetic Anisotropy Energy of MoS$_2$-supported Mn$_{12}$ complexes by Electric Field: A First-Principles Study

In this work, we examine low-energy adsorption configurations of four dodecanuclear manganese single-molecule magnets [Mn$_{12}$O$_{12}$(O$_2$CR)$_{16}$(H$_2$O)$_4$] (Mn$_{12}$), where the ligand R being H, CH$_3$, CHCl$_2$ or C$_6$H$_5$, on a molybdenum disulfide (MoS$_2$) monolayer using force field and density functional theory calculations. The van der Waals interaction is shown to be crucial for determining the adsorption energy. Some electrons transfer from the substrate to the molecules upon surface adsorption, resulting in a reduction of the magnetic anisotropy energy of Mn$_{12}$. Since the lowest unoccupied molecular orbital of Mn$_{12}$ is close to the valence band of MoS$_2$, a negative electric field is more effective in modulating charge transfer and energy band alignment, and thus altering the magnetic anisotropy energy, compared with a positive electric field. A significant increase in the magnetic anisotropy energy of Mn$_{12}$ with the ligand R=CHCl$_2$ or R=C$_6$H$_5$ under a sufficiently high electric field has been predicted. Our calculations show that the molecules remain intact on the surface both before and after the electric field is applied. Finally, a two-level system formed by different adsorption configurations is evaluated, and the tunability of its energy barrier under an electric field is demonstrated. Our study sheds light on tuning the properties of single-molecule magnets using an electric field, when the molecules are supported on a surface.

cond-mat.mtrl-sci↗

arXiv2025

FPGA-based Acceleration for Convolutional Neural Networks: A Comprehensive Review

Convolutional Neural Networks (CNNs) are fundamental to deep learning, driving applications across various domains. However, their growing complexity has significantly increased computational demands, necessitating efficient hardware accelerators. Field-Programmable Gate Arrays (FPGAs) have emerged as a leading solution, offering reconfigurability, parallelism, and energy efficiency. This paper provides a comprehensive review of FPGA-based hardware accelerators specifically designed for CNNs. It presents and summarizes the performance evaluation framework grounded in existing studies and explores key optimization strategies, such as parallel computing, dataflow optimization, and hardware-software co-design. It also compares various FPGA architectures in terms of latency, throughput, compute efficiency, power consumption, and resource utilization. Finally, the paper highlights future challenges and opportunities, emphasizing the potential for continued innovation in this field.

cs.LG↗

arXiv2024

Enhancing Dropout-based Bayesian Neural Networks with Multi-Exit on FPGA

Reliable uncertainty estimation plays a crucial role in various safety-critical applications such as medical diagnosis and autonomous driving. In recent years, Bayesian neural networks (BayesNNs) have gained substantial research and industrial interests due to their capability to make accurate predictions with reliable uncertainty estimation. However, the algorithmic complexity and the resulting hardware performance of BayesNNs hinder their adoption in real-life applications. To bridge this gap, this paper proposes an algorithm and hardware co-design framework that can generate field-programmable gate array (FPGA)-based accelerators for efficient BayesNNs. At the algorithm level, we propose novel multi-exit dropout-based BayesNNs with reduced computational and memory overheads while achieving high accuracy and quality of uncertainty estimation. At the hardware level, this paper introduces a transformation framework that can generate FPGA-based accelerators for the proposed efficient multi-exit BayesNNs. Several optimization techniques such as the mix of spatial and temporal mappings are introduced to reduce resource consumption and improve the overall hardware performance. Comprehensive experiments demonstrate that our approach can achieve higher energy efficiency compared to CPU, GPU, and other state-of-the-art hardware implementations. To support the future development of this research, we have open-sourced our code at: https://github.com/os-hxfan/MCME_FPGA_Acc.git

cs.LG↗

arXiv2022

Magnetic molecule tunnel heterojunctions

We characterize molecular magnet heterojunctions in which sublimated CoPc films as thin as 5 nm are sandwiched between transparent conducting bottom-layer indium tin oxide and top-layer soft-landing eutectic GaIn (EGaIn) electrodes. The roughness of the cobalt phthalocyanine (CoPc) films was determined by atomic force microscopy to be on the order of several nanometers, and crystalline ordering of lying-down planar molecules was confirmed by X-ray diffraction. The current-voltage (I-V) characteristics reveal the onset of a superconducting gap at Tc = 6 K, which together with higher temperature fits to a modified Simmons' model, provide incontrovertible evidence for direct quantum mechanical tunneling processes through the magnetic molecules in our heterojunctions. The voltage dependent features in the differential conductance measurements relate to spin states of single molecules or aggregates of molecules and should prove to be important for quantum information device development.

cond-mat.mes-hall↗

arXiv2022

Clar's goblet on graphene: field modulated charge transfer in a hydrocarbon heterostructure

In certain configurations, the aromatic properties of benzene ring structured molecules allow for unpaired, reactive valence electrons (known as radicals). Clar's goblets are such molecules. With an even number of unpaired radicals, these nanographenes are topologically frustrated hydrocarbons in which pi-bonding network and topology of edges give rise to the magnetism. Clar's goblets are therefore valued as prospective qubits provided they can be modulated between magnetic states. Using first principles DFT, we demonstrate the effects of adsorption on both molecule and substrate in a graphene-Clar's goblet heterostructure. We look at the energy difference bewteen FM and AFM states of the system and discuss underlying physical and chemical mechanisms in reference to the highest occupied molecular orbital (HOMO) and second HOMO (HOMO-1). We find that the HOMO of the molecule in the FM state is right at the Fermi surface, which leads to the hybridization between molecular state and the graphene state near the Dirac point. Furthermore, we investigate qualitative changes in charge realignment and magnetic state under variable electric field. Transitions from FM to AFM and back to FM states are observed.

cond-mat.mes-hall↗

arXiv2022

Insights to negative differential resistance in \texorpdfstring{MoS\textsubscript{2}}{MoS2} Esaki diodes: a first-principles perspective

\ce{MoS_2} is a two dimensional material with a band gap depending on the number of layers and tunable by an external electric field. The experimentally observed intralayer band-to-band tunneling and interlayer band-to-band tunneling in this material present an opportunity for new electronic applications in tunnel field effect transistors. However, such a widely accepted concept has never been supported up by theoretical investigations based on first principles. In this work, using density functional theory, in conjunction with non-equilibrilibrium Green's function techniques and our electric field gating method, enabled by a large-scale computational approach, we study the relation between band alignment and transmission in planar and side-stack \ce{MoS_2} $p$-$i$-$n$ junction configurations. We demonstrate the presence of negative differential resistance for both in-plane and interlayer current, a staple characteristic of tunnel diode junctions, and analyze the physical origin of such an effect. Electrostatic potentials, the van der Waals barrier, and complex band analysis are also examined for a thorough understanding of Esaki Diodes.

cond-mat.mes-hall↗

arXiv2022

Single-Molecule Magnet Mn$_{12}$ on GaAs-supported Graphene: Gate Field Effects From First Principles

We study gate field effects on the Mn$_{12}$O$_{12}$(COOH)$_{16}$(H$_2$O)$_4$ | graphene | GaAs heterostructure via first-principles calculations. We find that under moderate doping levels electrons can be added to but not taken from the single-molecule magnet Mn$_{12}$O$_{12}$(COOH)$_{16}$(H$_2$O)$_4$ (Mn$_{12}$). The magnetic anisotropy energy (MAE) of Mn$_{12}$ decreases as the electron doping level increases, due to electron transfer from graphene to Mn$_{12}$ and change in the band alignment between Mn$_{12}$ and graphene. At an electron doping level of $-5.00 \times 10^{13}\, \textrm{cm}^{-2}$, the MAE decreases by about 18% compared with zero doping. The band alignment between graphene and GaAs is more sensitive to electron doping than to hole doping since the valence band of GaAs is close to the Fermi level. The GaAs substrate induces a small bandgap in the supported graphene under the zero gate field and a nearly strain-free configuration. Finally, we propose a vertical tunnel junction for probing the gate dependence of MAE via electron transport measurements.

cond-mat.mtrl-sci↗

arXiv2021

Algorithm and Hardware Co-design for Reconfigurable CNN Accelerator

Recent advances in algorithm-hardware co-design for deep neural networks (DNNs) have demonstrated their potential in automatically designing neural architectures and hardware designs. Nevertheless, it is still a challenging optimization problem due to the expensive training cost and the time-consuming hardware implementation, which makes the exploration on the vast design space of neural architecture and hardware design intractable. In this paper, we demonstrate that our proposed approach is capable of locating designs on the Pareto frontier. This capability is enabled by a novel three-phase co-design framework, with the following new features: (a) decoupling DNN training from the design space exploration of hardware architecture and neural architecture, (b) providing a hardware-friendly neural architecture space by considering hardware characteristics in constructing the search cells, (c) adopting Gaussian process to predict accuracy, latency and power consumption to avoid time-consuming synthesis and place-and-route processes. In comparison with the manually-designed ResNet101, InceptionV2 and MobileNetV2, we can achieve up to 5% higher accuracy with up to 3x speed up on the ImageNet dataset. Compared with other state-of-the-art co-design frameworks, our found network and hardware configuration can achieve 2% ~ 6% higher accuracy, 2x ~ 26x smaller latency and 8.5x higher energy efficiency.

cs.LG↗

arXiv2020

Multiple Control of Few-layer Janus MoSSe Systems

In this computational work based on density functional theory we study the electronic and electron transport properties of asymmetric multi-layer MoSSe junctions, known as Janus junctions. Focusing on 4-layer systems, we investigate the influence of electric field, electrostatic doping, strain, and interlayer stacking on the electronic structure. We discover that a metal to semiconductor transition can be induced by an out-of-plane electric field. The critical electric field for such a transition can be reduced by in-plane biaxial compressive strain. Due to an intrinsic electric field, a 4-layer MoSSe can rectify out-of-plane electric current. The rectifying ratio reaches 34.1 in a model junction Zr/4-layer MoSSe/Zr. This ratio can be further enhanced by increasing the number of MoSSe layers. In addition, we show a drastic sudden vertical compression of 4-layer MoSSe due to in-plane biaxial tensile strain, indicating a second phase transition. Furthermore, an odd-even effect on electron transmission at the Fermi energy for Zr/$n$-layer MoSSe/Zr junctions with $n=1, \, 2,\, 3, \,\dots,\, 10$ is observed. These findings reveal the richness of physics in this asymmetric system and strongly suggest that the properties of 4-layer MoSSe are highly tunable, thus providing a guide to future experiments relating materials research and nanoelectronics.

cond-mat.mtrl-sci↗

arXiv2020

First-Principles study of an S = 1 quasi-1D quantum molecular magnetic material

We use density functional theory to study the structural, magnetic and electronic structure of the organo-metallic quantum magnet $\mathrm{NiCl_2-4SC(NH_2)_2}$ (DTN). Recent work has demonstrated the quasi-1D nature of the molecular crystal and its quantum phase transitions at low temperatures. This includes a magneto-electric coupling and, when doped with Br, the presence of an exotic Bose-glass state. We systematically show that, by using the generalized gradient approximation (GGA) with inclusion of a van der Waals term to account for weak inter-molecular forces and by introducing a Hubbard $U$ term to the total energy, our calculations reproduce the magnetic anisotropy, the inter-molecular exchange coupling strength and the magneto-electric effect in DTN, which were observed in previous experiments. Further analysis into the electronic structure gives insight into the underlying magnetic interactions, including what mechanisms may be causing the ME effect. Using this computationally efficient model, we predict what effect applying an electric field might have on the magnetic properties of this quantum magnet.

cond-mat.mes-hall↗

arXiv2020

Gate field effects on the topological insulator BiSbTeSe2 interface

Interfaces between two topological insulators are of fundamental interest in condensed matter physics. Inspired by experimental efforts, we study interfacial processes between two slabs of BiSbTeSe2 (BSTS) via first principles calculations. Topological surface states are absent for the BSTS interface at its equilibrium separation, but our calculations show that they appear if the inter-slab distance is greater than 6 Ang. More importantly, we find that topological interface states can be preserved by inserting two or more layers of hexagonal boron nitride between the two BSTS slabs. In experiments, the electric current tunneling through the interface is insensitive to back gate voltage when the bias voltage is small. Using a first-principles based method that allows us to simulate gate field, we show that at low bias the extra charge induced by a gate voltage resides on the surface that is closest to the gate electrode, leaving the interface almost undoped. This provides clues to understand the origin of the observed insensitivity of transport properties to back voltage at low bias. Our study resolves a few questions raised in experiment, which does not yet offer a clear correlation between microscopic physics and transport data. We provide a road map for the design of vertical tunneling junctions involving the interface between two topological insulators.

cond-mat.mtrl-sci↗

arXiv2020

First-principles study of magnetism and electric field effects in 2D systems

This review article provides a bird's-eye view of what first-principles based methods can contribute to next-generation device design and simulation. After a brief overview of methods and capabilities in the area, we focus on published work by our group since 2015 and current work on $\textrm{CrI}_3$. We introduce both single- and dual-gate models in the framework of density functional theory and the constrained random phase approximation in estimating the Hubbard $U$ for 2D systems vs. their 3D counterparts. A wide range of systems, including graphene-based heterogeneous systems, transition metal dichalcogenides, and topological insulators, and a rich array of physical phenomena, including the macroscopic origin of polarization, field effects on magnetic order, interface state resonance induced peak in transmission coefficients, spin filtration, etc., are covered. For $\textrm{CrI}_3$ we present our new results on bilayer systems such as the interplay between stacking and magnetic order, pressure dependence, and electric field induced magnetic phase transitions. We find that a bare bilayer $\textrm{CrI}_3$, graphene$\,|\,$bilayer $\textrm{CrI}_3\,|\,$graphene, $h$-BN$\,|\,$bilayer $\textrm{CrI}_3\,|\,h$-BN, and $h$-BN$\,|\,$bilayer $\textrm{CrI}_3\,|\,$graphene all have a different response at high field, while small field the difference is small except for graphene$\,|\,$bilayer $\textrm{CrI}_3\,|\,$graphene. We conclude with discussion of some ongoing work and work planned in the near future, with the inclusion of further method development and applications.

cond-mat.mes-hall↗

arXiv2020

First-principles calculation of gate-tunable ferromagnetism in magic-angle twisted bilayer graphene under pressure

Magic-angle twisted bilayer graphene (MATBG) is notable as a highly tunable platform for investigating strongly correlated phenomena such as high-$T_c$ superconductivity and quantum spin liquids, due to easy control of doping level through gating and sensitive dependence of the magic angle on hydrostatic pressure. Experimental observations of correlated insulating states, unconventional superconductivity and ferromagnetism in MATBG indicate that this system exhibits rich exotic phases. In this work, using density functional theory calculations in conjunction with the effective screening medium method, we find the MATBG under pressure at a twisting angle of $2.88\unicode{xb0}$ and simulate how its electronic states evolve when doping level and out-of-plane electric field are gate-tuned. Our calculations show that, at doping levels between two electrons and four holes per moiré unit cell, a ferromagnetic solution with spin density localized at AA stacking sites is lower in energy than the nonmagnetic solution. The magnetic moment of this ferromagnetic state decreases with both electron and hole doping and vanishes at four electrons/holes doped per moiré unit cell. Hybridization between the flat bands at the Fermi level and the surrounding dispersive bands can take place at finite doping. Moreover, upon increasing the out-of-plane electric field at zero doping, a transition from the ferromagnetic state to the nonmagnetic one is seen. We also analyze the interlayer bonding character due to the flat bands via Wannier functions. Finally, we report trivial band topology of the flat bands in the ferromagnetic state at a certain doping level.

cond-mat.mes-hall↗

arXiv2019

Optimizing CNN-based Hyperspectral Image Classification on FPGAs

Hyperspectral image (HSI) classification has been widely adopted in applications involving remote sensing imagery analysis which require high classification accuracy and real-time processing speed. Methods based on Convolutional neural networks (CNNs) have been proven to achieve state-of-the-art accuracy in classifying HSIs. However, CNN models are often too computationally intensive to achieve real-time response due to the high dimensional nature of HSI, compared to traditional methods such as Support Vector Machines (SVMs). Besides, previous CNN models used in HSI are not specially designed for efficient implementation on embedded devices such as FPGAs. This paper proposes a novel CNN-based algorithm for HSI classification which takes into account hardware efficiency. A customized architecture which enables the proposed algorithm to be mapped effectively onto FPGA resources is then proposed to support real-time on-board classification with low power consumption. Implementation results show that our proposed accelerator on a Xilinx Zynq 706 FPGA board achieves more than 70x faster than an Intel 8-core Xeon CPU and 3x faster than an NVIDIA GeForce 1080 GPU. Compared to previous SVM-based FPGA accelerators, we achieve comparable processing speed but provide a much higher classification accuracy.

eess.IV↗