A machine learning tight-binding framework reconstructs DFT-level Hamiltonians and computes electronic properties for systems with up to 100 million atoms, including graphene mobility versus carrier concentration.
Hu et al., Observation of Rydberg moiré excitons, Science 380, 1367 (2023)
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GPUTB: Efficient Machine Learning Tight-Binding Method for Large-Scale Electronic Properties Calculations
A machine learning tight-binding framework reconstructs DFT-level Hamiltonians and computes electronic properties for systems with up to 100 million atoms, including graphene mobility versus carrier concentration.