arXiv subjects

Chen Hua

Publications and source records attributed to Chen Hua.

3 recordsLinked to original sources

arXiv2026

GPUMDkit: A User-Friendly Toolkit for GPUMD and NEP

Machine-learned interatomic potentials have revolutionized molecular dynamics simulations by providing quantum-mechanical accuracy at empirical-potential speeds. The graphics processing unit molecular dynamics (GPUMD) package, featuring the highly efficient neuroevolution potential (NEP) framework, has emerged as a powerful tool in this domain. However, the complexity of force field development, active learning, and trajectory post-processing often requires extensive manual scripting, imposing a steep learning curve on new users. To address this, we present GPUMDkit, a comprehensive and user-friendly toolkit that streamlines the entire simulation workflow for GPUMD and NEP. GPUMDkit integrates a suite of essential functionalities, including format conversion, structure sampling, property calculation, and data visualization, accessible through both interactive and command-line interfaces. Its modular, extensible architecture ensures accessibility for users of all experience levels while allowing seamless integration of new features. By automating complex tasks and enhancing productivity, GPUMDkit substantially lowers the barrier to using GPUMD and NEP programs. This article describes the program architecture and demonstrates its capabilities through practical applications.

cond-mat.mtrl-sci↗

arXiv2026

Liquid Metals Routes towards Making Superconductors

We conceive liquid-metal-derived superconductors (LMDS) as a unified paradigm that enables the quick fabrication of superconducting materials under near-ambient conditions through introducing room-temperature liquid metals (LMs) as dynamic metallic reaction media. In this framework, LMs serve as solvents, dopant reservoirs, interfacial mediators, and structural templates that lower the barrier to forming superconducting-relevant material states. This paradigm integrates LM-enabled pathways for producing bulk alloys, printed films, two-dimensional confined phases, interconnect geometries, and nanodroplets. Their liquid-state processability enables near-room-temperature patterning, reconfiguration, and compositional control, whereas superconducting functionality is established in cooled LM-derived states such as solidified alloys, doped films, amorphous/glassy phases, nanoconfined structures, and interfacially reconstructed layers. We further outline a data-driven LM materials genome that unifies composition, structure, ground-state quantities, interaction parameters, and macroscopic properties to accelerate predictive modeling and inverse design of LMDS. Beyond processing advantages, LMs provide an experimental platform for examining superconductivity in amorphous, nanoconfined, and dynamically disordered states and for revisiting the longstanding question of whether true superconductivity can exist in liquid state. This perspective positions LMs as a fertile and energy-efficient route toward reconfigurable and potentially transformative superconducting technologies.

cond-mat.supr-con↗

arXiv2025

Tree Models Machine Learning to Identify Liquid Metal based Alloy Superconductor

Superconductors, which are crucial for modern advanced technologies due to their zero-resistance properties, are limited by low Tc and the difficulty of accurate prediction. This article made the initial endeavor to apply machine learning to predict the critical temperature (Tc) of liquid metal (LM) alloy superconductors. Leveraging the SuperCon dataset, which includes extensive superconductor property data, we developed a machine learning model to predict Tc. After addressing data issues through preprocessing, we compared multiple models and found that the Extra Trees model outperformed others with an R2 of 0.9519 and an RMSE of 6.2624 K. This model is subsequently used to predict Tc for LM alloys, revealing In0.5Sn0.5 as having the highest Tc at 7.01 K. Furthermore, we extended the prediction to 2,145 alloys binary and 45,670 ternary alloys across 66 metal elements and promising results were achieved. This work demonstrates the advantages of tree-based models in predicting Tc and would help accelerate the discovery of high-performance LM alloy superconductors in the coming time.

cond-mat.supr-con↗