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Accurately Generalizing Deep-Learning Hamiltonian Methods to Plane-Wave Density Functional Theory
Deep neural networks that predict density functional theory (DFT) Hamiltonians from material structures offer a route to efficient electronic-structure calculations. However, extending localized atomic-orbital (AO) Hamiltonian learning to plane-wave (PW) calculations with hybrid functionals requires
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