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Journal Articles

Self-learning path integral hybrid Monte Carlo with mixed ${it ab initio}$ and machine learning potentials for modeling nuclear quantum effects in water

Thomsen, B.; Nagai, Yuki*; Kobayashi, Keita; Hamada, Ikutaro*; Shiga, Motoyuki

Journal of Chemical Physics, 161(20), p.204109_1 - 204109_18, 2024/11

 Times Cited Count:0 Percentile:0.00(Chemistry, Physical)

We introduce the self-learning path integral hybrid Monte Carlo with mixed ${it ab initio}$ and machine learning potentials (SL-PIHMC-MIX) method which allows the application of hybrid Monte Carlo for both path integrals and for larger system sizes. The method shows savings of an order of magnitude with respect to the number of ${it ab initio}$ DFT calculations needed to calculate and converge the structure of room temperature water when using SL-PIHMC-MIX over ab initio path integral molecular dynamics (PIMD).

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Self-regeneration of perovskite-based catalyst, 1; Crystal structure

Taniguchi, Masashi*; Kan, C. Y.*; Uenishi, Mari*; Tanaka, Hirohisa*; Matsumura, Daiju; Nishihata, Yasuo; Mizuki, Junichiro; Uozumi, Akifumi*; Hamada, Ikutaro*; Morikawa, Yoshitada*

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