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Numerical experiments suggest that our proposed methodology allows achieving accuracy that is comparable to other popular interpolation and machine learning techniques and requires significantly less time for model training than nonlinearly parameterized formulation.We survey the underlying theory behind the large-scale and linear scaling density functional theory code, conquest, which shows excellent parallel scaling and can be applied to thousands of atoms with diagonalization and millions of atoms with linear scaling. We give details

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