A variational framework for computing Wannier functions using dictionary learning

doi:10.1016/j.jcp.2021.110793

2022-04-01

Bradley Magnetta *

Vidvuds Ozoliņš

DOI：10.1016/j.jcp.2021.110793

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摘要

En 中文

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.

Keyword:

Wannier functions

Variational principle

Dictionary learning

Machine learning

Optimization

Data-driven

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期刊

Journal of Computational Physics

IF:

3.8

论文数:

1.5W

被引数:

7.4W

机构

Yale University

学者数:

6.5W

论文数: 6.0W

被引数: 10.0W

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