Literature DB >> 30306168

Determination of possible configurations for Li0.5CoO2 delithiated Li-ion battery cathodes via DFT calculations coupled with a multi-objective non-dominated sorting genetic algorithm (NSGA-III).

Woo Gyu Han1, Woon Bae Park1, Satendra Pal Singh1, Myoungho Pyo2, Kee-Sun Sohn1.   

Abstract

Here, we propose a new and logical approach to systematically treat the configurational diversity in density functional theory (DFT) calculations. To tackle this issue, we select Li0.5CoO2 as a representative example because it is one of the most extensively studied cathodes in Li-ion batteries (LIBs), and it has a huge number of disordered configurations. To delineate the configurations that will match well with the experimentally measured macro-functions of redox potential, band gap energy, and magnetic moment, we adopt a multi-objective, non-dominated sorting, genetic algorithm (NSGA-III) that enables the simultaneous optimization of these three objective functions. The decision variables include configuration of the Li/vacancy, initial input for the magnetic moment distribution reflecting Co3+/Co4+ distribution, and initial input for the lattice parameter and Hubbard U. We use NSGA-III to separate the configurations that exhibit awkward objective function values, which allows us to pinpoint a set of plausible configurations that match the experimentally estimated values of the objective functions. The results reveal a plausible configuration that is a mixture of various ordered/disordered configurations rather than a simple ordered structure.

Entities:  

Year:  2018        PMID: 30306168     DOI: 10.1039/c8cp05284k

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.  Aliovalent-doped sodium chromium oxide (Na0.9Cr0.9Sn0.1O2 and Na0.8Cr0.9Sb0.1O2) for sodium-ion battery cathodes with high-voltage characteristics.

Authors:  Woon Bae Park; Muthu Gnana Theresa Nathan; Su Cheol Han; Jin-Woong Lee; Kee-Sun Sohn; Myoungho Pyo
Journal:  RSC Adv       Date:  2020-11-27       Impact factor: 4.036

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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