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Argyrodite configuration determination for DFT and AIMD calculations using an integrated optimization strategy.
Lee, Byung Do; Lee, Jin-Woong; Park, Joonseo; Cho, Min Young; Park, Woon Bae; Sohn, Kee-Sun.
Afiliación
  • Lee BD; Faculty of Nanotechnology and Advanced Materials Engineering, Sejong University Seoul 05006 Republic of Korea kssohn@sejong.ac.kr.
  • Lee JW; Faculty of Nanotechnology and Advanced Materials Engineering, Sejong University Seoul 05006 Republic of Korea kssohn@sejong.ac.kr.
  • Park J; Faculty of Nanotechnology and Advanced Materials Engineering, Sejong University Seoul 05006 Republic of Korea kssohn@sejong.ac.kr.
  • Cho MY; Faculty of Nanotechnology and Advanced Materials Engineering, Sejong University Seoul 05006 Republic of Korea kssohn@sejong.ac.kr.
  • Park WB; Department of Advanced Components and Materials Engineering, Sunchon National University Chonnam 57922 Republic of Korea wbpark@scnu.ac.kr.
  • Sohn KS; Faculty of Nanotechnology and Advanced Materials Engineering, Sejong University Seoul 05006 Republic of Korea kssohn@sejong.ac.kr.
RSC Adv ; 12(48): 31156-31166, 2022 Oct 27.
Article en En | MEDLINE | ID: mdl-36349042
When constructing a partially occupied model structure for use in density functional theory (DFT) and ab initio molecular dynamics (AIMD) calculations, the selection of appropriate configurations has been a vexing issue. Random sampling and the ensuing low-Coulomb-energy entry selection have been routine. Here, we report a more efficient way of selecting low-Coulomb-energy configurations for a representative solid electrolyte, Li6PS5Cl. Metaheuristics (genetic algorithm, particle swarm optimization, cuckoo search, and harmony search), Bayesian optimization, and modified deep Q-learning are utilized to search the large configurational space. Ten configuration candidates that exhibit relatively low Coulomb energy values and thereby lead to more convincing DFT and AIMD calculation results are pinpointed along with computational cost savings by the assistance of the above-described optimization algorithms, which constitute an integrated optimization strategy. Consequently, the integrated optimization strategy outperforms the conventional random sampling-based selection strategy.

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: RSC Adv Año: 2022 Tipo del documento: Article Pais de publicación: Reino Unido

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: RSC Adv Año: 2022 Tipo del documento: Article Pais de publicación: Reino Unido