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Stratified construction of neural network based interatomic models for multicomponent materials

Hajinazar, Samad ; Shao, Junping ; Kolmogorov, Aleksey N.

Physical review. B, 2017-01, Vol.95 (1), p.014114, Article 014114 [Periódico revisado por pares]

College Park: American Physical Society

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  • Título:
    Stratified construction of neural network based interatomic models for multicomponent materials
  • Autor: Hajinazar, Samad ; Shao, Junping ; Kolmogorov, Aleksey N.
  • Assuntos: Copper ; Density functional theory ; Free energy ; Heat of formation ; Local optimization ; Neural networks ; Palladium ; Silver ; Training
  • É parte de: Physical review. B, 2017-01, Vol.95 (1), p.014114, Article 014114
  • Descrição: Recent application of neural networks (NNs) to modeling interatomic interactions has shown the learning machines' encouragingly accurate performance for select elemental and multicomponent systems. In this study we explore the possibility of building a library of NN-based models by introducing a hierarchical NN training. In such a stratified procedure NNs for multicomponent systems are obtained by sequential training from the bottom up: first unaries, then binaries, and so on. Advantages of constructing NN sets with shared parameters include acceleration of the training process and intact description of the constituent systems. We use an automated generation of diverse structure sets for NN training on density functional theory-level reference energies. In the test case of Cu, Pd, Ag, Cu-Pd, Cu-Ag, Pd-Ag, and Cu-Pd-Ag systems, NNs trained in the traditional and stratified fashions are found to have essentially identical accuracy for defect energies, phonon dispersions, formation energies, etc. The models' robustness is further illustrated via unconstrained evolutionary structure searches in which the NN is used for the local optimization of crystal unit cells.
  • Editor: College Park: American Physical Society
  • Idioma: Inglês
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  • Realização: ABCD-USP USP   Logos de Redes Sociais: Freepik

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