463 citations · 787 across the 17 of their papers we have counts for
8 papers · 1 filter
Think Globally, Generate Personally: Multi-View Thinking with Adaptive Generation for Generative Recommendation
Yuxuan Hu, Yingyi Zhang, Wenlin Zhang +11
Semantic-ID-based (SID) generative recommendation (GR) represents each item as a sequence of discrete tokens and predicts the next item through autoregressive SID generation. Howev…
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…