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- W3211988902 abstract "This article introduces a data-driven variational framework for computing Wannier functions by using basis pursuit to transfer information from a general feature dictionary. General features are learned by applying dictionary learning to a dataset of Wannier functions. Our approach displays a systematically controllable energy-localization trade-off, an objective functional allowing for the use of fast numerical solvers, and is capable of being used in highly-efficient self-consistent algorithms." @default.
- W3211988902 created "2021-11-22" @default.
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- W3211988902 date "2022-04-01" @default.
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- W3211988902 title "A variational framework for computing Wannier functions using dictionary learning" @default.
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- W3211988902 doi "https://doi.org/10.1016/j.jcp.2021.110793" @default.
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