arXiv subjects

Zhongyang Wang

Publications and source records attributed to Zhongyang Wang.

6 recordsLinked to original sources

arXiv2026

The HTC-Claw: Automating Discovery through High-Throughput Computational Campaigns

With the advancement of the Materials Genome Initiative, high-throughput computation has become central to accelerating materials discovery. However, conventional first-principles workflows are cumbersome and error-prone. Existing high-throughput tools, while efficient at batch job submission, lack intelligence: they cannot automatically plan tasks based on scientific objectives or dynamically adapt workflows according to intermediate results. To address these limitations, this paper proposes and implements HTC-Claw, an intelligent high-throughput computational platform built upon the OpenClaw framework. The key innovations of HTC-Claw are: 1) An agent-based framework for automatic decomposition of high-level research goals into parallelizable task sets; 2) A closed-loop execution engine that integrates real-time analysis and reporting; 3) Adaptive decision-making and workflow iteration capabilities based on intermediate results; and 4) A decoupled, modular architecture that separates the scheduling system from functional modules, enhancing extensibility and robustness. Case studies demonstrate that HTC-Claw enables an intelligent, end-to-end workflow from user intent to final reporting in materials exploration

cond-mat.mtrl-sci↗

arXiv2025

IEC-Independent Coupling Between Water Uptake and Ionic Conductivity in Anion-Conducting Polymer Films

Anion exchange membranes (AEMs) are promising candidates for replacing proton exchange membranes (PEMs) in electrochemical devices such as fuel cells, electrolyzers, batteries, and osmotic energy extraction systems. However, optimizing the AEM design requires a deeper understanding of the ionic conduction mechanism in the hydrated polymer matrix. This study investigates this mechanism by seeking to understand the relationship between ion exchange capacity (IEC), water absorption, and ionic conductivity in polynorbornene-based thin films. We combine experimental measurements with computational simulations using a newly developed minimal model of the polymer film. Our model is able to reproduce key experimental observations, including water sorption isotherms and ion conduction behavior as a function of relative humidity, and successfully captures the relationship between them. By comparing experimental data with computational results, we explain the commonly reported correlation between conductivity and hydration level and show how the correlation between these variables is affected by the charge density and temperature of the material. Our research advances our understanding of the physical mechanisms that govern the performance of the polyelectrolyte membrane, which is essential for the development of more efficient, stable, and environmentally friendly electrochemical systems.

cond-mat.soft↗

arXiv2024

EF-LLM: Energy Forecasting LLM with AI-assisted Automation, Enhanced Sparse Prediction, Hallucination Detection

Accurate prediction helps to achieve supply-demand balance in energy systems, supporting decision-making and scheduling. Traditional models, lacking AI-assisted automation, rely on experts, incur high costs, and struggle with sparse data prediction. To address these challenges, we propose the Energy Forecasting Large Language Model (EF-LLM), which integrates domain knowledge and temporal data for time-series forecasting, supporting both pre-forecast operations and post-forecast decision-support. EF-LLM's human-AI interaction capabilities lower the entry barrier in forecasting tasks, reducing the need for extra expert involvement. To achieve this, we propose a continual learning approach with updatable LoRA and a multi-channel architecture for aligning heterogeneous multimodal data, enabling EF-LLM to continually learn heterogeneous multimodal knowledge. In addition, EF-LLM enables accurate predictions under sparse data conditions through its ability to process multimodal data. We propose Fusion Parameter-Efficient Fine-Tuning (F-PEFT) method to effectively leverage both time-series data and text for this purpose. EF-LLM is also the first energy-specific LLM to detect hallucinations and quantify their occurrence rate, achieved via multi-task learning, semantic similarity analysis, and ANOVA. We have achieved success in energy prediction scenarios for load, photovoltaic, and wind power forecast.

cs.LG↗

arXiv2019

Single frame wide-field Nanoscopy based on Ghost Imaging via Sparsity Constraints (GISC Nanoscopy)

The applications of present nanoscopy techniques for live cell imaging are limited by the long sampling time and low emitter density. Here we developed a new single frame wide-field nanoscopy based on ghost imaging via sparsity constraints (GISC Nanoscopy), in which a spatial random phase modulator is applied in a wide-field microscopy to achieve random measurement for fluorescence signals. This new method can effectively utilize the sparsity of fluorescence emitters to dramatically enhance the imaging resolution to 80 nm by compressive sensing (CS) reconstruction for one raw image. The ultra-high emitter density of 143 μm-2 has been achieved while the precision of single-molecule localization below 25 nm has been maintained. Thereby working with high-density of photo-switchable fluorophores GISC nanoscopy can reduce orders of magnitude sampling frames compared with previous single-molecule localization based super-resolution imaging methods.

physics.optics↗

arXiv2018

Recoil-ion momentum spectroscopy of photoionization of cold rubidium atoms in a strong laser field

We study photoionization of cold rubidium atoms in a strong infrared laser field using a magneto-optical trap (MOT) recoil ion momentum spectrometer. Three types of cold rubidium target are provided, operating in two-dimension (2D) MOT, 2D molasses, and 3D MOT with densities in the orders of $10^7$ atoms/cm$^3$, $10^8$ atoms/cm$^3$, and $10^9$ atoms/cm$^3$, respectively. The density profile and the temperature of 3D MOT are characterized using the absorption imaging and photoionization. The momentum distributions of Rb$^+$ created by absorption of two- or three-photon illuminate a dipole-like double-peak structure, in good agreement with the results in the strong field approximation. The yielding momentum resolution of $0.12 \pm 0.03$ a.u. is achieved in comparison with theoretical calculations, exhibiting the great prospects for the study of electron correlations in alkali metal atoms through interaction with strong laser pulses.

quant-ph↗

arXiv2000

Quantum key distribution relied on trusted information center

Quantum correlation between two particles and among three particles show nonclassic properties that can be used for providing secure transmission of information. In this paper, we propose two quantum key distribution schemes for quantum cryptographic network, which use the correlation properties of two and three particles. One is implemented by the Greenberger-Horne-Zeilinger state, and another is implemented by the Bell states. These schemes need a trusted information center like that in the classic cryptography. The optimal efficiency of the proposed protocols are higher than that in the previous schemes.

quant-ph↗