Machine learning method for tight-binding Hamiltonian parameterization from ab-initio band structure

Abstract The tight-binding (TB) method is an ideal candidate for determining electronic and transport properties for a large-scale system. It describes the system as real-space Hamiltonian matrices expressed on a manageable number of parameters, leading to substantially lower computational costs tha...

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Autores principales:	Zifeng Wang, Shizhuo Ye, Hao Wang, Jin He, Qijun Huang, Sheng Chang
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Materials of engineering and construction. Mechanics of materials TA401-492 Computer software QA76.75-76.765
Acceso en línea:	https://doaj.org/article/5828919a2b55495b889aabfc55773a47
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spelling	oai:doaj.org-article:5828919a2b55495b889aabfc55773a472021-12-02T13:24:35ZMachine learning method for tight-binding Hamiltonian parameterization from ab-initio band structure10.1038/s41524-020-00490-52057-3960https://doaj.org/article/5828919a2b55495b889aabfc55773a472021-01-01T00:00:00Zhttps://doi.org/10.1038/s41524-020-00490-5https://doaj.org/toc/2057-3960Abstract The tight-binding (TB) method is an ideal candidate for determining electronic and transport properties for a large-scale system. It describes the system as real-space Hamiltonian matrices expressed on a manageable number of parameters, leading to substantially lower computational costs than the ab-initio methods. Since the whole system is defined by the parameterization scheme, the choice of the TB parameters decides the reliability of the TB calculations. The typical empirical TB method uses the TB parameters directly from the existing parameter sets, which hardly reproduces the desired electronic structures quantitatively without specific optimizations. It is thus not suitable for quantitative studies like the transport property calculations. The ab-initio TB method derives the TB parameters from the ab-initio results through the transformation of basis functions, which achieves much higher numerical accuracy. However, it assumes prior knowledge of the basis and may encompass truncation error. Here, a machine learning method for TB Hamiltonian parameterization is proposed, within which a neural network (NN) is introduced with its neurons acting as the TB matrix elements. This method can construct the empirical TB model that reproduces the given ab-initio energy bands with predefined accuracy, which provides a fast and convenient way for TB model construction and gives insights into machine learning applications in physical problems.Zifeng WangShizhuo YeHao WangJin HeQijun HuangSheng ChangNature PortfolioarticleMaterials of engineering and construction. Mechanics of materialsTA401-492Computer softwareQA76.75-76.765ENnpj Computational Materials, Vol 7, Iss 1, Pp 1-10 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Materials of engineering and construction. Mechanics of materials TA401-492 Computer software QA76.75-76.765
spellingShingle	Materials of engineering and construction. Mechanics of materials TA401-492 Computer software QA76.75-76.765 Zifeng Wang Shizhuo Ye Hao Wang Jin He Qijun Huang Sheng Chang Machine learning method for tight-binding Hamiltonian parameterization from ab-initio band structure
description	Abstract The tight-binding (TB) method is an ideal candidate for determining electronic and transport properties for a large-scale system. It describes the system as real-space Hamiltonian matrices expressed on a manageable number of parameters, leading to substantially lower computational costs than the ab-initio methods. Since the whole system is defined by the parameterization scheme, the choice of the TB parameters decides the reliability of the TB calculations. The typical empirical TB method uses the TB parameters directly from the existing parameter sets, which hardly reproduces the desired electronic structures quantitatively without specific optimizations. It is thus not suitable for quantitative studies like the transport property calculations. The ab-initio TB method derives the TB parameters from the ab-initio results through the transformation of basis functions, which achieves much higher numerical accuracy. However, it assumes prior knowledge of the basis and may encompass truncation error. Here, a machine learning method for TB Hamiltonian parameterization is proposed, within which a neural network (NN) is introduced with its neurons acting as the TB matrix elements. This method can construct the empirical TB model that reproduces the given ab-initio energy bands with predefined accuracy, which provides a fast and convenient way for TB model construction and gives insights into machine learning applications in physical problems.
format	article
author	Zifeng Wang Shizhuo Ye Hao Wang Jin He Qijun Huang Sheng Chang
author_facet	Zifeng Wang Shizhuo Ye Hao Wang Jin He Qijun Huang Sheng Chang
author_sort	Zifeng Wang
title	Machine learning method for tight-binding Hamiltonian parameterization from ab-initio band structure
title_short	Machine learning method for tight-binding Hamiltonian parameterization from ab-initio band structure
title_full	Machine learning method for tight-binding Hamiltonian parameterization from ab-initio band structure
title_fullStr	Machine learning method for tight-binding Hamiltonian parameterization from ab-initio band structure
title_full_unstemmed	Machine learning method for tight-binding Hamiltonian parameterization from ab-initio band structure
title_sort	machine learning method for tight-binding hamiltonian parameterization from ab-initio band structure
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/5828919a2b55495b889aabfc55773a47
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Machine learning method for tight-binding Hamiltonian parameterization from ab-initio band structure

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