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cond-mat.mtrl-sci2026
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…
cond-mat.mtrl-sci2025
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…
cond-mat.mtrl-sci2023
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…