Database of Wannier tight-binding Hamiltonians using high-throughput density functional theory

Abstract

Wannier tight-binding Hamiltonians (WTBH) provide a computationally efficient way to predict electronic properties of materials. In this work, we develop a computational workflow for high-throughput Wannierization of density functional theory (DFT) based electronic band structure calculations. We apply this workflow to 1771 materials (1406 3D and 365 2D), and we create a database with the resulting WTBHs. We evaluate the accuracy of the WTBHs by comparing the Wannier band structures to directly calculated spin-orbit coupling DFT band structures. Our testing includes k-points outside the grid used in the Wannierization, providing an out-of-sample test of accuracy. We illustrate the use of WTBHs with a few example applications. We also develop a web-app that can be used to predict electronic properties on-the-fly using WTBH from our database. The tools to generate the Hamiltonian and the database of the WTB parameters are made publicly available through the websites https://github.com/usnistgov/jarvis and https://jarvis.nist.gov/jarviswtb.

Bound States In and Out of the Continuum in Nanoribbons with Wider Sections: A Novel Algorithm based on the Recursive S‐Matrix Method

“…To analyze specific systems, it is only necessary to set adequate parameters in the tight-binding Hamiltonian. Realistic parameters can be obtained from a Wannierization of density func-tional theory band structure calculations, [38,39] and from the density functional-based tight-binding method. [40][41][42][43] Recent developments as the use of neural networks may also provide a path to obtain more realistic parameters for arbitrary systems.…”

A critical examination of robustness and generalizability of machine learning prediction of materials properties

“…12 materials science datasets. High-throughput density functional theory (DFT) calculations have proven to be an efficient and reliable way to generate materials property data, screen the target materials space and accelerate materials discovery [13][14][15][16][17] . Concentrated community efforts have led to the curation of large DFT databases for various materials properties, e.g., Materials Project 18 , Automatic FLOW for Materials Discovery 19 , Open Quantum Materials Database 14 , and JARVIS-DFT 20 .…”

Equivariant analytical mapping of first principles Hamiltonians to accurate and transferable materials models

“…Most commonly, electronic structure simulations of materials are performed in non-atom-centred basis representations such as the pseudopotential plane wave framework, which is not easily amenable to the construction of TB models. TB Hamiltonians are typically constructed via transformation into a maximally localised Wannier function representation 21 , which provides a compact atom-centred basis representation with local support 22 . It is also possible to fit Slater-Koster parameters directly to DFT calculations in a data-driven fashion 23,24 .…”