463 citations · 787 across the 17 of their papers we have counts for
17 papers
Cartesian tensor equivariant machine-learning force field for spin-dependent atomistic simulations
Junjie Wang, Yijie Zhu, Zhongwei Zhang +5
Magnetic materials exhibit an intricate coupling between atomic structure and spin degrees of freedom, posing a fundamental challenge for atomistic simulations across experimentall…
Superconducting ternary compounds Li-X-B (X=Mo, W) within the mild pressure range: First-principles predictions
Bangshuai Zhu, Juefei Wu, Dexi Shao +6
Among the superconducting hydrides under high pressure, a number of studies concentrate on the ternary compounds to explore unique superconductors, which are capable of reducing th…
High-order tensor neural network for iteration-free structure relaxation
Shaobo Yu, Haoting Zhang, Yu Han +5
Structure relaxation is important for the discovery of new materials, yet conventional ab initio optimization remains a major bottleneck in high-throughput screening workflows. Mac…
Identifying sensitivity-dominant parameters via active subspaces in reduced-order modeling of fluid dynamics
Dewu Yang, Rui Wang, Pengyu Lai +3
Reduced-order models (ROMs) are widely employed to describe complex system dynamics when simulations with full-order models (FOMs) are computationally prohibitive. This study prese…
Differentiable Particle-Mesh Ewald with Cartesian Tensor Message Passing for Learning Long-Range Electrostatics and Dipole Response
Zhiyue Guo, Junjie Wang, Haoting Zhang +4
Machine learning interatomic potentials (MLIPs) can approach quantum accuracy for short-range chemistry, but most architectures remain local and fail to capture the long-range elec…
NEPMaker: Active learning of neuroevolution machine learning potential for large cells
Junjie Wang, Shuning Pan, Haoting Zhang +4
Machine learning potentials (MLPs) achieve near first-principles accuracy but often fail for atomic environments outside the training distribution. Active learning can mitigate thi…
GPUTB-2:An efficient E(3) network method for learning high-precision orthogonal Hamiltonian
Yunlong Wang, Zhixin Liang, Chi Ding +5
Although equivariant neural networks have become a cornerstone for learning electronic Hamiltonians, the intrinsic non-orthogonality of linear combinations of atomic orbitals (LCAO…
Super-hard and superconducting boron clathrates in the prediction of U-B compounds
Juefei Wu, Dexi Shao, Junjie Wang +6
The binary metal borides provide a promising platform for searching unique materials with superconductivity and super-hardness under high pressure, owing to the distinctive bonding…
GPU-MetaD: Full-Life-Cycle GPU Accelerated Metadynamics with Machine Learning Potentials
Haoting Zhang, Qiuhan Jia, Zhennan Zhang +6
Large-scale molecular dynamics simulations with high accuracy have been increasingly popular for their capability to bridge the gap between atomistic modeling and mesoscale phenome…
GPUTB: Efficient Machine Learning Tight-Binding Method for Large-Scale Electronic Properties Calculations
Yunlong Wang, Zhixin Liang, Chi Ding +5
The high computational cost of ab-initio methods limits their application in predicting electronic properties at the device scale. Therefore, an efficient method is needed to map t…
E(n)-Equivariant Cartesian Tensor Passing Potential
Junjie Wang, Yong Wang, Haoting Zhang +6
Machine learning potential (MLP) has been a popular topic in recent years for its potential to replace expensive first-principles calculations in some large systems. Meanwhile, mes…
General-purpose machine-learned potential for 16 elemental metals and their alloys
Keke Song, Rui Zhao, Jiahui Liu +25
Machine-learned potentials (MLPs) have exhibited remarkable accuracy, yet the lack of general-purpose MLPs for a broad spectrum of elements and their alloys limits their applicabil…
Prediction of surface reconstructions using MAGUS
Yu Han, Junjie Wang, Chi Ding +4
In this paper, we present a new module to predict the potential surface reconstruction configurations of given surface structures in the framework of our machine learning and graph…
GPUMD: A package for constructing accurate machine-learned potentials and performing highly efficient atomistic simulations
Zheyong Fan, Yanzhou Wang, Penghua Ying +17
We present our latest advancements of machine-learned potentials (MLPs) based on the neuroevolution potential (NEP) framework introduced in [Fan et al., Phys. Rev. B 104, 104309 (2…
Partially Diffusive Helium-Silica Compound in the Deep Interiors of Giant Planets
Cong Liu, Junjie Wang, Xin Deng +7
Helium is the second most abundant element in the universe, and together with silica, they are major components of giant planets. Exploring the reactivity and state of helium and s…
Enhancing Crystal Structure Prediction by decomposition methods based on graph theory
Hao Gao, Junjie Wang, Yu Han +1
Crystal structure prediction algorithms have become powerful tools for materials discovery in recent years, however, they are usually limited to relatively small systems. The main…
Dimensionalities and multiplicities determination of crystal nets
Hao Gao, Junjie Wang, Zhaopeng Guo +1
Low-dimensional materials have attracted significant attentions over the past decade. To discover new low-dimensional materials, high-throughout screening methods have been applied…