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A variational framework for computing Wannier functions using dictionary learning
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DOI：10.1016/j.jcp.2021.110793.png)
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Abstract
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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 tradeoff, an objective functional allowing for the use of fast numerical solvers, and is capable of being used in highly-efficient self-consistent algorithms.(c) 2021 Elsevier Inc. All rights reserved.
Keywords:
Wannier functions
Variational principle
Dictionary learning
Machine learning
Optimization
Data-driven
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