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Title: Characterization of Thin Film Materials using SCAN meta-GGA, an Accurate Nonempirical Density Functional

Abstract

We discuss self-consistently obtained ground-state electronic properties of monolayers of graphene and a number of ’beyond graphene’ compounds, including films of transition-metal dichalcogenides (TMDs), using the recently proposed strongly constrained and appropriately normed (SCAN) meta-generalized gradient approximation (meta-GGA) to the density functional theory. The SCAN meta-GGA results are compared with those based on the local density approximation (LDA) as well as the generalized gradient approximation (GGA). As expected, the GGA yields expanded lattices and softened bonds in relation to the LDA, but the SCAN meta-GGA systematically improves the agreement with experiment. Our study suggests the efficacy of the SCAN functional for accurate modeling of electronic structures of layered materials in high-throughput calculations more generally.

Authors:
 [1];  [1];  [1];  [2];  [3];  [1]
  1. Northeastern Univ., Boston, MA (United States). Dept. of Physics
  2. Temple Univ., Philadelphia, PA (United States). Dept. of Physics
  3. Univ. of Texas, El Paso, TX (United States). Dept. of Physics
Publication Date:
Research Org.:
Energy Frontier Research Centers (EFRC) (United States). Center for Complex Materials from First Principles (CCM); Northeastern Univ., Boston, MA (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Basic Energy Sciences (BES); Center for Computational Design of Functional Layered Materials (CCDM); Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States). National Energy Research Scientific Computing Center (NERSC)
OSTI Identifier:
1423570
Grant/Contract Number:  
FG02-07ER46352; AC02-05CH11231; SC0012575
Resource Type:
Accepted Manuscript
Journal Name:
Scientific Reports
Additional Journal Information:
Journal Volume: 7; Journal ID: ISSN 2045-2322
Publisher:
Nature Publishing Group
Country of Publication:
United States
Language:
English
Subject:
36 MATERIALS SCIENCE; Electronic properties and materials; Surfaces; interfaces and thin films

Citation Formats

Buda, I. G., Lane, C., Barbiellini, B., Ruzsinszky, A., Sun, J., and Bansil, A.. Characterization of Thin Film Materials using SCAN meta-GGA, an Accurate Nonempirical Density Functional. United States: N. p., 2017. Web. https://doi.org/10.1038/srep44766.
Buda, I. G., Lane, C., Barbiellini, B., Ruzsinszky, A., Sun, J., & Bansil, A.. Characterization of Thin Film Materials using SCAN meta-GGA, an Accurate Nonempirical Density Functional. United States. https://doi.org/10.1038/srep44766
Buda, I. G., Lane, C., Barbiellini, B., Ruzsinszky, A., Sun, J., and Bansil, A.. Thu . "Characterization of Thin Film Materials using SCAN meta-GGA, an Accurate Nonempirical Density Functional". United States. https://doi.org/10.1038/srep44766. https://www.osti.gov/servlets/purl/1423570.
@article{osti_1423570,
title = {Characterization of Thin Film Materials using SCAN meta-GGA, an Accurate Nonempirical Density Functional},
author = {Buda, I. G. and Lane, C. and Barbiellini, B. and Ruzsinszky, A. and Sun, J. and Bansil, A.},
abstractNote = {We discuss self-consistently obtained ground-state electronic properties of monolayers of graphene and a number of ’beyond graphene’ compounds, including films of transition-metal dichalcogenides (TMDs), using the recently proposed strongly constrained and appropriately normed (SCAN) meta-generalized gradient approximation (meta-GGA) to the density functional theory. The SCAN meta-GGA results are compared with those based on the local density approximation (LDA) as well as the generalized gradient approximation (GGA). As expected, the GGA yields expanded lattices and softened bonds in relation to the LDA, but the SCAN meta-GGA systematically improves the agreement with experiment. Our study suggests the efficacy of the SCAN functional for accurate modeling of electronic structures of layered materials in high-throughput calculations more generally.},
doi = {10.1038/srep44766},
journal = {Scientific Reports},
number = ,
volume = 7,
place = {United States},
year = {2017},
month = {3}
}

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