40 citations · 164 across the 11 of their papers we have counts for
11 papers
Inelastic Neutron Scattering of Hydrogen in Palladium Studied by Semiclassical Dynamics
Motoyuki Shiga, Bo Thomsen, Hajime Kimizuka
Inelastic neutron scattering (INS) spectra of hydrogen in face-centered cubic palladium have been calculated considering nuclear quantum effects (NQE) at finite temperatures. The c…
Path Integral Brownian Chain Molecular Dynamics: A Simple Approximation of Quantum Vibrational Dynamics
Motoyuki Shiga
An approximate approach to quantum vibrational dynamics, "Brownian Chain Molecular Dynamics (BCMD)", is proposed to alleviate the chain resonance and curvature problems in the imag…
Nuclear quantum effect on the elasticity of ice VII under pressure: A path-integral molecular dynamics study
Jun Tsuchiya, Motoyuki Shiga, Shinji Tsuneyuki +1
We investigate the effect of nuclear quantum effects (NQEs) of hydrogen atoms on the elasticity of ice VII at high pressure and ambient temperature conditions using ab initio path-…
Self-learning hybrid Monte Carlo method for isothermal-isobaric ensemble: Application to liquid silica
Keita Kobayashi, Yuki Nagai, Mitsuhiro Itakura +1
Self-learning hybrid Monte Carlo (SLHMC) is a first-principles simulation that allows for exact ensemble generation on potential energy surfaces based on density functional theory.…
Mean Force Based Temperature Accelerated Sliced Sampling: Efficient Reconstruction of High Dimensional Free Energy Landscapes
Asit Pal, Subhendu Pal, Shivani Verma +2
Temperature Accelerated Sliced Sampling (TASS) is an efficient method to compute high dimensional free energy landscapes. The original TASS method employs the Weighted Histogram An…
Nuclear Quantum Effects on Autoionization of Water Isotopologues Studied by Ab Initio Path Integral Molecular Dynamics
Bo Thomsen, Motoyuki Shiga
In this study we investigate the nuclear quantum effects (NQEs) on the acidity constant (pKA) of liquid water isotopologues at the ambient condition by path integral molecular dyna…
Self-learning Hybrid Monte Carlo: A First-principles Approach
Yuki Nagai, Masahiro Okumura, Keita Kobayashi +1
We propose a novel approach called Self-Learning Hybrid Monte Carlo (SLHMC) which is a general method to make use of machine learning potentials to accelerate the statistical sampl…
A screened automated structural search with semiempirical methods
Yukihiro Ota, Sergi Ruiz-Barragan, Masahiko Machida +1
We developed an interface program between a program suite for an automated search of chemical reaction pathways, GRRM, and a program package of semiempirical methods, MOPAC. A two-…
On the hierarchical parallelization of ab initio simulations
Sergi Ruiz-Barragan, Kazuya Ishimura, Motoyuki Shiga
A hierarchical parallelization has been implemented in a new unified code PIMD-SMASH for ab initio simulation where the replicas and the Born-Oppenheimer forces are parallelized. I…
Multiscale enhanced path sampling based on the Onsager-Machlup action: Application to a model polymer
Hiroshi Fujisaki, Motoyuki Shiga, Kei Moritsugu +1
We propose a novel path sampling method based on the Onsager-Machlup (OM) action by generalizing the multiscale enhanced sampling (MSES) technique suggested by Moritsugu and cowork…
Onsager-Machlup action-based path sampling and its combination with replica exchange for diffusive and multiple pathways
Hiroshi Fujisaki, Motoyuki Shiga, Akinori Kidera
For sampling multiple pathways in a rugged energy landscape, we propose a novel action-based path sampling method using the Onsager-Machlup action functional. Inspired by the Fouri…