4 papers
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…
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…
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…
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…