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.
Song et al., General -purpose machine-learned potential for 16 elemental metals and their alloys, Nat Commun 15, 10208 (2024)
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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.