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Title: Diffusion Monte Carlo: A pathway towards an accurate theoretical description of manganese oxides

Abstract

Here, we present diffusion Monte Carlo (DMC) results for equation of state and quasiparticle gaps of manganese binary oxides MnO and MnO 2 and the ternary oxide LaMnO 3. Owing to the limited approximations made and the direct treatment of electronic correlations, our DMC-based study correctly describes structural properties such as the lattice constant, bulk moduli, and cohesive energies. It correctly predicts the ground-state phase of these oxides, which have different valences. Our study demonstrates the capability of DMC methods to predict the structural properties of highly correlated systems, which have been identified as suitable candidates for many applications ranging from catalysis to electronic devices. Our study also serves as a benchmark for both the manganese pseudopotential and other methodological choices to be used in calculations of similar oxides.

Authors:
ORCiD logo [1]; ORCiD logo [1]; ORCiD logo [2]; ORCiD logo [1]
  1. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Materials Science & Technology Division
  2. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Center for Nanophase Materials Science (CNMS) and Computational Science and Engineering Division
Publication Date:
Research Org.:
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Oak Ridge Leadership Computing Facility (OLCF)
Sponsoring Org.:
USDOE Office of Science (SC), Basic Energy Sciences (BES) (SC-22). Materials Sciences & Engineering Division
OSTI Identifier:
1474475
Alternate Identifier(s):
OSTI ID: 1464575
Grant/Contract Number:  
AC05-00OR22725
Resource Type:
Journal Article: Accepted Manuscript
Journal Name:
Physical Review Materials
Additional Journal Information:
Journal Volume: 2; Journal Issue: 8; Journal ID: ISSN 2475-9953
Publisher:
American Physical Society (APS)
Country of Publication:
United States
Language:
English
Subject:
36 MATERIALS SCIENCE; 37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY; diffusion quantum Monte Carlo

Citation Formats

Saritas, Kayahan, Krogel, Jaron T., Kent, P. R. C., and Reboredo, Fernando A. Diffusion Monte Carlo: A pathway towards an accurate theoretical description of manganese oxides. United States: N. p., 2018. Web. doi:10.1103/PhysRevMaterials.2.085801.
Saritas, Kayahan, Krogel, Jaron T., Kent, P. R. C., & Reboredo, Fernando A. Diffusion Monte Carlo: A pathway towards an accurate theoretical description of manganese oxides. United States. doi:10.1103/PhysRevMaterials.2.085801.
Saritas, Kayahan, Krogel, Jaron T., Kent, P. R. C., and Reboredo, Fernando A. Tue . "Diffusion Monte Carlo: A pathway towards an accurate theoretical description of manganese oxides". United States. doi:10.1103/PhysRevMaterials.2.085801. https://www.osti.gov/servlets/purl/1474475.
@article{osti_1474475,
title = {Diffusion Monte Carlo: A pathway towards an accurate theoretical description of manganese oxides},
author = {Saritas, Kayahan and Krogel, Jaron T. and Kent, P. R. C. and Reboredo, Fernando A.},
abstractNote = {Here, we present diffusion Monte Carlo (DMC) results for equation of state and quasiparticle gaps of manganese binary oxides MnO and MnO2 and the ternary oxide LaMnO3. Owing to the limited approximations made and the direct treatment of electronic correlations, our DMC-based study correctly describes structural properties such as the lattice constant, bulk moduli, and cohesive energies. It correctly predicts the ground-state phase of these oxides, which have different valences. Our study demonstrates the capability of DMC methods to predict the structural properties of highly correlated systems, which have been identified as suitable candidates for many applications ranging from catalysis to electronic devices. Our study also serves as a benchmark for both the manganese pseudopotential and other methodological choices to be used in calculations of similar oxides.},
doi = {10.1103/PhysRevMaterials.2.085801},
journal = {Physical Review Materials},
issn = {2475-9953},
number = 8,
volume = 2,
place = {United States},
year = {2018},
month = {8}
}

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