Predictive evaluation of hydrogen diffusion coefficient on Pd(111) surface by path integral simulations using neural network potential

Kataoka, Yuta; Haruyama, Jun; Sugino, Osamu; Shiga, Motoyuki

doi:https://doi.org/10.1103/PhysRevResearch.6.043224

Predictive evaluation of hydrogen diffusion coefficient on Pd(111) surface by path integral simulations using neural network potential

Kataoka, Yuta; Haruyama, Jun; Sugino, Osamu et al.

Physical Review Research, 2024, 6, 043224

アクセス数:257件（2026-02-15 23:50 集計）

固定URL: https://hdl.handle.net/11094/99691

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タイトル	Predictive evaluation of hydrogen diffusion coefficient on Pd(111) surface by path integral simulations using neural network potential
著者	著者名 Kataoka, Yuta
	NRID 1000080772003 1000080772003 著者名 Haruyama, Jun
	NRID 1000090361659 1000090361659 著者名 Sugino, Osamu
	NRID 1000040370407 1000040370407 著者名 Shiga, Motoyuki
内容	Kataoka Y., Haruyama J., Sugino O., et al. Predictive evaluation of hydrogen diffusion coefficient on Pd(111) surface by path integral simulations using neural network potential. Physical Review Research 6, 043224 (2024); https://doi.org/10.1103/PhysRevResearch.6.043224.
抄録	Quantum diffusion of hydrogen (H) on palladium (Pd) (111) was studied using two types of path integral simulations: quantum transition state theory (QTST) and ring polymer molecular dynamics (RPMD). The use of an artificial neural network potential trained by density functional theory calculations has made it feasible to perform path integral simulations considering nuclear quantum effects (NQEs) of a many-body Pd-H system with ab initio accuracy. The QTST result has shown a clear non-Arrhenius behavior in the diffusion coefficient (D) of H below the temperature of 150-200 K due to the NQEs. Comparing the D on Pd surface and bulk Pd, it was found that surface and bulk diffusion are competitive at high temperature. Consistent with this, it was observed in the RPMD simulation at 800 K that a part of the quantum trajectories of H on the surface bifurcates to the Pd subsurface. As the temperature decreases, the surface diffusion becomes much faster than the bulk diffusion. Furthermore, distinct quantum behaviors of H were identified at the surface and within the bulk. The D values obtained from this study using a "flexible"Pd surface model differed considerably from those previously reported using "frozen"Pd surface models, indicating an important contribution from Pd vibrations coupled with H diffusion.
出版者	American Physical Society
収録物名	Physical Review Research
巻(号)	6
ページ	043224
刊行年月	2024-12-02
言語	英語
ハンドルURL	https://hdl.handle.net/11094/99691
PISSN	26431564
関連情報 (isIdenticalTo)	DOI (出版社版) https://doi.org/10.1103/PhysRevResearch.6.043224
アクセス権	open access
権利情報	This article is licensed under a Creative Commons Attribution 4.0 International License.
出版タイプ	Version of Record
カテゴリ	学術雑誌論文 Journal Article

資源タイプ	学術雑誌論文
ローカル資源タイプ	学術雑誌論文
DCTERMS.bibliographicCitation	Physical Review Research. 2024, 6, p. 043224
DC.title	Predictive evaluation of hydrogen diffusion coefficient on Pd(111) surface by path integral simulations using neural network potential
DC.creator	Kataoka, Yuta
	Haruyama, Jun
	Sugino, Osamu
	Shiga, Motoyuki
DC.publisher	American Physical Society
DC.language' scheme='DCTERMS.RFC1766	英語
DCTERMS.issued' scheme='DCTERMS.W3CDTF	2024-12-02
DC.identifier	https://doi.org/10.1103/PhysRevResearch.6.043224
DC.description	Kataoka Y., Haruyama J., Sugino O., et al. Predictive evaluation of hydrogen diffusion coefficient on Pd(111) surface by path integral simulations using neural network potential. Physical Review Research 6, 043224 (2024); https://doi.org/10.1103/PhysRevResearch.6.043224.
DCTERMS.abstract	Quantum diffusion of hydrogen (H) on palladium (Pd) (111) was studied using two types of path integral simulations: quantum transition state theory (QTST) and ring polymer molecular dynamics (RPMD). The use of an artificial neural network potential trained by density functional theory calculations has made it feasible to perform path integral simulations considering nuclear quantum effects (NQEs) of a many-body Pd-H system with ab initio accuracy. The QTST result has shown a clear non-Arrhenius behavior in the diffusion coefficient (D) of H below the temperature of 150-200 K due to the NQEs. Comparing the D on Pd surface and bulk Pd, it was found that surface and bulk diffusion are competitive at high temperature. Consistent with this, it was observed in the RPMD simulation at 800 K that a part of the quantum trajectories of H on the surface bifurcates to the Pd subsurface. As the temperature decreases, the surface diffusion becomes much faster than the bulk diffusion. Furthermore, distinct quantum behaviors of H were identified at the surface and within the bulk. The D values obtained from this study using a "flexible"Pd surface model differed considerably from those previously reported using "frozen"Pd surface models, indicating an important contribution from Pd vibrations coupled with H diffusion.
DC.rights	This article is licensed under a Creative Commons Attribution 4.0 International License.
DC.format	99691
citation_title	Predictive evaluation of hydrogen diffusion coefficient on Pd(111) surface by path integral simulations using neural network potential
citation_author	Kataoka, Yuta
	Haruyama, Jun
	Sugino, Osamu
	Shiga, Motoyuki
citation_publisher	American Physical Society
citation_language	英語
citation_date	2024-12-02
citation_journal_title	Physical Review Research
citation_volume	6
citation_firstpage	043224
citation_doi	https://doi.org/10.1103/PhysRevResearch.6.043224
citation_public_url	https://hdl.handle.net/11094/99691

Predictive evaluation of hydrogen diffusion coefficient on Pd(111) surface by path integral simulations using neural network potential

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