Literature DB >> 28621354

Density functional theory calculations for the band gap and formation energy of Pr4-xCaxSi12O3+xN18-x; a highly disordered compound with low symmetry and a large cell size.

Sung Un Hong1, Satendra Pal Singh, Myoungho Pyo, Woon Bae Park, Kee-Sun Sohn.   

Abstract

A novel oxynitride compound, Pr4-xCaxSi12O3+xN18-x, synthesized using a solid-state route has been characterized as a monoclinic structure in the C2 space group using Rietveld refinement on synchrotron powder X-ray diffraction data. The crystal structure of this compound was disordered due to the random distribution of Ca/Pr and N/O ions at various Wyckoff sites. A pragmatic approach for an ab initio calculation based on density function theory (DFT) for this disordered compound has been implemented to calculate an acceptable value of the band gap and formation energy. In general, for the DFT calculation of a disordered compound, a sufficiently large super cell and infinite variety of ensemble configurations is adopted to simulate the random distribution of ions; however, such an approach is time consuming and cost ineffective. Even a single unit cell model gave rise to 43 008 independent configurations as an input model for the DFT calculations. Since it was nearly impossible to calculate the formation energy and the band gap energy for all 43 008 configurations, an elitist non-dominated sorting genetic algorithm (NSGA-II) was employed to find the plausible configurations. In the NSGA-II, all 43 008 configurations were mathematically treated as genomes and the calculated band gap and the formation energy as the objective (fitness) function. Generalized gradient approximation (GGA) was first employed in the preliminary screening using NSGA-II, and thereafter a hybrid functional calculation (HSE06) was executed only for the most plausible GGA-relaxed configurations with lower formation and higher band gap energies. The final band gap energy (3.62 eV) obtained after averaging over the selected configurations, resembles closely the experimental band gap value (4.11 eV).

Entities:  

Year:  2017        PMID: 28621354     DOI: 10.1039/c7cp03247a

Source DB:  PubMed          Journal:  Phys Chem Chem Phys        ISSN: 1463-9076            Impact factor:   3.676


  3 in total

1.  Dirty engineering data-driven inverse prediction machine learning model.

Authors:  Jin-Woong Lee; Woon Bae Park; Byung Do Lee; Seonghwan Kim; Nam Hoon Goo; Kee-Sun Sohn
Journal:  Sci Rep       Date:  2020-11-24       Impact factor: 4.379

2.  Ylide-Stabilized Phosphenium Cations: Impact of the Substitution Pattern on the Coordination Chemistry.

Authors:  Tobias Stalder; Felix Krischer; Henning Steinert; Philipp Neigenfind; Viktoria H Gessner
Journal:  Chemistry       Date:  2022-01-05       Impact factor: 5.020

3.  A machine-learning-based alloy design platform that enables both forward and inverse predictions for thermo-mechanically controlled processed (TMCP) steel alloys.

Authors:  Jin-Woong Lee; Chaewon Park; Byung Do Lee; Joonseo Park; Nam Hoon Goo; Kee-Sun Sohn
Journal:  Sci Rep       Date:  2021-05-26       Impact factor: 4.379

  3 in total

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