139 citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- Xiamen UniversityCN14 papers
- Fujian Normal UniversityCN8 papers
- Collaborative Innovation Center of Chemistry for Energy MaterialsCN6 papers
- Chinese Academy of SciencesCN4 papers
- Fuzhou UniversityCN4 papers
- Anhui UniversityCN2 papers
- Dalian University of TechnologyCN2 papers
- Fujian Institute of Research on the Structure of MatterCN2 papers
- Fujian Science and Technology Innovation Laboratory for Optoelectronic Information of ChinaCN2 papers
- Institute of Applied Physics and Computational MathematicsCN2 papers
- Institute of Technology of CambodiaKH2 papers
- National University of SingaporeSG2 papers
28 papers
TXL Fusion: A Hybrid Machine Learning Framework Integrating Chemical Heuristics and Large Language Models for Topological Materials Discovery
Arif Ullah, Rajibul Islam, Yangming Huang +4
Topological materials, including topological insulators (TIs) and topological semimetals (TSMs), offer promising platforms for quantum, spintronic, and low-dissipation electronic t…
Gravity-Induced Modulation of Negative Differential Thermal Resistance in Fluids
Qiyuan Zhang, Juncheng Guo, Juchang Zou +1
We investigate how gravity influences negative differential thermal resistance (NDTR) in fluids modeled by multiparticle collision dynamics. In the integrable case, we derive the h…
Machine Learning Accelerated Computational Surface-Specific Vibrational Spectroscopy Reveals Oxidation Level of Graphene in Contact with Water
Xianglong Du, Jun Cheng, Fujie Tang
Precise characterization of the graphene/water interface has been hindered by experimental inconsistencies and limited molecular-level access to interfacial structures. In this wor…
Hierarchical graph sampling based minibatch learning with chain preservation and variance reduction
Qia Hu, Bo Jiao
Graph sampling-based Graph Convolutional Networks (GCNs) decouple sampling from forward and backward propagation during minibatch training, enhancing scalability with respect to la…
Hartree-Fock approximation for bosons with symmetry-adapted variational wave functions
B. R. Que, J. M. Zhang, H. F. Song +1
The Hartree-Fock approximation for bosons employs variational wave functions that are a combination of permanents. These are bosonic counterpart of the fermionic Slater determinant…
Stabilizing Single-Atom Catalysts on Metastable Phases of Transition Metal Dichalcogenides
Lina Wang, Zhenhai Wen, Guangfu Luo
Single-atom catalysts have attracted significant attention due to their exceptional atomic utilization and high efficiency in a range of catalytic reactions. However, these systems…
Decoding the Competing Effects of Dynamic Solvation Structures on Nuclear Magnetic Resonance Chemical Shifts of Battery Electrolytes via Machine Learning
Qi You, Yan Sun, Feng Wang +2
Understanding the solvation structure of electrolytes is critical for optimizing the electrochemical performance of rechargeable batteries, as it directly influences properties suc…
Auto-resolving the atomic structure at van der Waals interfaces using a generative model
Wenqiang Huang, Yuchen Jin, Zhemin Li +12
The high-resolution visualization of atomic structures is significant for understanding the relationship between the microscopic configurations and macroscopic properties of materi…
Revealing the molecular structures of a-Al2O3(0001)-water interface by machine learning based computational vibrational spectroscopy
Xianglong Du, Weizhi Shao, Chenglong Bao +3
Solid-water interfaces are crucial to many physical and chemical processes and are extensively studied using surface-specific sum-frequency generation (SFG) spectroscopy. To establ…
MLatom software ecosystem for surface hopping dynamics in Python with quantum mechanical and machine learning methods
Lina Zhang, Sebastian V. Pios, Mikołaj Martyka +7
We present an open-source MLatom@XACS software ecosystem for on-the-fly surface hopping nonadiabatic dynamics based on the Landau-Zener-Belyaev-Lebedev (LZBL) algorithm. The dynami…
Tell machine learning potentials what they are needed for: Simulation-oriented training exemplified for glycine
Fuchun Ge, Ran Wang, Chen Qu +6
Machine learning potentials (MLPs) are widely applied as an efficient alternative way to represent potential energy surfaces (PES) in many chemical simulations. The MLPs are often…
Physics-informed active learning for accelerating quantum chemical simulations
Yi-Fan Hou, Lina Zhang, Quanhao Zhang +2
Quantum chemical simulations can be greatly accelerated by constructing machine learning potentials, which is often done using active learning (AL). The usefulness of the construct…
Energy bands in a three dimension simple cubic lattice of contact potential
Yi-Cai Zhang, J. M. Zhang
In this work, we investigate energy bands in a three dimensional simple cubic lattice of contact potential. The energy bands in the first Brillouin Zone are obtained with Ewald's s…
Four-Dimensional-Spacetime Atomistic Artificial Intelligence Models
Fuchun Ge, Lina Zhang, Yi-Fan Hou +3
We demonstrate that AI can learn atomistic systems in the four-dimensional (4D) spacetime. For this, we introduce the 4D-spacetime GICnet model which for the given initial conditio…
Energy-conserving molecular dynamics is not energy conserving
Lina Zhang, Yi-Fan Hou, Fuchun Ge +1
Molecular dynamics (MD) is a widely-used tool for simulating the molecular and materials properties. It is a common wisdom that molecular dynamics simulations should obey physical…
pH-dependent water permeability switching and its memory in 1T' MoS membranes
C. Hu, A. Achari, P. Rowe +13
Intelligent transport of molecular species across different barriers is critical for various biological functions and is achieved through the unique properties of biological membra…
Saliency-Aware Spatio-Temporal Artifact Detection for Compressed Video Quality Assessment
Liqun Lin, Yang Zheng, Weiling Chen +2
Compressed videos often exhibit visually annoying artifacts, known as Perceivable Encoding Artifacts (PEAs), which dramatically degrade video visual quality. Subjective and objecti…
Search for heavy Majorana neutrinos in the final state at proton-electron colliders
Haiyong Gu, Ying-nan Mao, Hao Sun +1
We utilize the lepton number violation signal process to search for heavy Majorana neutrinos at future proton-electron colliders. The LHeC (FCC-eh) is conside…
Microwave Amplification in a PT -symmetric-like Cavity Magnomechanical System
Hua Jin, Zhi-Bo Yang, Jing-Wen Jin +3
We propose a scheme that can generate tunable magnomechanically induced amplification in a double-cavity parity-time-(PT -) symmetric-like magnomechanical system under a strong con…
Structure Parameter Optimized Kernel Based Online Prediction with a Generalized Optimization Strategy for Nonstationary Time Series
Jinhua Guo, Hao Chen, Jingxin Zhang +1
In this paper, sparsification techniques aided online prediction algorithms in a reproducing kernel Hilbert space are studied for nonstationary time series. The online prediction a…
Permanent variational wave functions for bosons
J. M. Zhang, H. F. Song, Y. Liu
We study the performance of permanent states (the bosonic counterpart of the Slater determinant state) as approximating functions for bosons, with the intention to develop variatio…
Heat conduction in a three-dimensional momentum-conserving fluid
Rongxiang Luo, Lisheng Huang, Stefano Lepri
Size-dependence of energy transport and the effects of reduced dimensionality on transport coefficients are of key importance for understanding nonequilibrium properties of matter…
Variable guiding strategies in multi-exits evacuation: Pursuing balanced pedestrian densities
Huan Ren, Yuyue Yan, Fengqiang Gao
Evacuation assistants and their guiding strategies play an important role in the multi-exits pedestrian evacuation. To investigate the effect of guiding strategies on evacuation ef…
Adsorption and ultrafast diffusion of lithium in bilayer graphene ab initio and kinetic Monte Carlo simulation study
Kehua Zhong, Ruina Hu, Guigui Xu +3
In this work, we adopt first-principle calculations based on density functional theory and Kinetic Monte Carlo simulations to investigate the adsorption and diffusion of lithium in…