arXiv subjects

Zhelong Jiang

Publications and source records attributed to Zhelong Jiang.

4 recordsLinked to original sources

arXiv2026

Electrochemical and thermal control of continuous phase transitions in P2-NaxNi1/3Mn2/3O2

Sodium layered oxides often undergo phase transformations involving ordering or disordering of Na+ upon desodiation, i.e., when cycled as a battery electrode. Accurately characterizing these phases is crucial for understanding functional properties, such as chemical diffusivity. In this work, we reveal that Na+-vacancy (dis)ordering in a layered oxide is intrinsically coupled to symmetry-changing phase transformations of the host structure. We examine the low-symmetry orthorhombic unit cell of P2-NaxNi1/3Mn2/3O2 (NNM) using both neutron and X-ray diffraction. Specifically, special sodium stoichiometries (x = 2/3 and 1/2) exhibit concomitant Na+-vacancy ordering and an orthorhombic distortion from the parent hexagonal unit cell. We then demonstrate that electrochemical desodiation drives symmetry-changing transformations in NNM that are linked to Na+-vacancy (dis)ordering, with evidence of second-order behavior observed near x = 2/3. Variable-temperature synchrotron X-ray diffraction further clarifies the coupling between Na+-vacancy disordering and orthorhombic-to-hexagonal phase transitions in NNM. Surprisingly, the temperature-driven phase transitions at x = 2/3 and 1/2 differ in character, appearing second-order and first-order, respectively. Our analysis of the phase transitions in NNM has fundamental consequences for sodium chemical diffusivity in the vicinity of the ordered phases and leads to design principles for modifying phase transition behavior in the broader class of intercalation electrodes.

cond-mat.mtrl-sci↗

arXiv2025

An RRAM compute-in-memory architecture for high energy-efficient processing of binary matrix-vector multiplication in cryptography

Binary matrix-vector multiplication (BMVM) is a key operation in post-quantum cryptography schemes like the Classic McEliece cryptosystem. Conventional computing architectures incur significant energy efficiency loss due to data movement of large matrices when handling such tasks. Resistive memory (RRAM) non-volatile compute-in-memory (nvCIM) is an ideal technology for high energy-efficient BMVM processing but faces challenges, including signal margin degradation in high input-parallelism arrays due to device non-idealities and high hardware overhead from current readout and XOR operations. This work presents a RRAM nvCIM architecture featuring: 1) 1T1R cells with high-resistive-state compensation modules; and 2) pulsed current-sensing parity checkers. Based on the 180nm process and test results from RRAM devices, the computing accuracy and efficiency of the architecture are verified by simulation. The proposed architecture performs high-precision current accumulation with a maximum MAC value of 10 and achieves an energy efficiency of 1.51TOPS/W, offering approximately 1.62 times improvement compared to an advanced 28nm FPGA platform.

cs.ET↗

arXiv2025

Electrochemically induced switching from antiferromagnetic spin-chain to frustrated spin-glass state in maple-leaf lattice Na2Mn3O7

We report the electrochemical tuning of magnetic properties in the Na2Mn3O7 maple-leaf lattice (MLL) through ion deintercalation, revealing a switch from the 1D antiferromagnetic (AFM) spin-chain behavior of the S=3/2 MLL structure to frustrated magnetism spin-glass behavior. By utilizing Na deintercalation, we stabilize ferromagnetic (FM) short-range interactions within the original short-range AFM system, creating magnetic frustration within the system beyond that induced from the MLL geometrically frustrated structure, leading to a spin-glass state. Magnetic and structural analyses, combined with density functional theory (DFT) calculations, demonstrate the near-degeneracy between AFM and FM configurations in Na2Mn3O7, suggesting that the altered lattice distortions and disorder introduced via deintercalation are responsible for the frustrated magnetism. Our findings provide a novel platform for studying low-dimensional magnetism, spin glass behavior, and potential applications in spintronics and computing technologies. This study represents the first observation of an induced spin glass state in MLL materials and is a rare example of electrochemically induced spin glass state, highlighting the critical role of ion intercalation in tuning magnetic interactions.

cond-mat.mtrl-sci↗

arXiv2023

Point convolutional neural network algorithm for Ising model ground state research based on spring vibration

The ground state search of the Ising model can be used to solve many combinatorial optimization problems. Under the current computer architecture, an Ising ground state search algorithm suitable for hardware computing is necessary for solving practical problems. Inspired by the potential energy conversion of springs, we propose a point convolutional neural network algorithm for ground state search based on spring vibration model, called Spring-Ising Algorithm. Spring-Ising Algorithm regards the spin as a moving mass point connected to a spring and establish the equation of motion for all spins. Spring-Ising Algorithm can be mapped on the GPU or AI chips through the basic structure of the neural network for fast and efficient parallel computing. The algorithm has very productive results for solving the Ising model and has been test in the recognized test benchmark K2000. The algorithm introduces the concept of dynamic equilibrium to achieve a more detailed local search by dynamically adjusting the weight of the Ising model in the spring oscillation model. Finally, there is the simple hardware test speed evaluation. Spring-Ising Algorithm can provide the possibility to calculate the Ising model on a chip which focuses on accelerating neural network calculations.

physics.comp-ph↗