143 citations · 909 across the 23 of their papers we have counts for
23 papers
Wetting hysteresis induces effective unidirectional water transport through a fluctuating nanochannel
Noriyoshi Arai, Eiji Yamamoto, Takahiro Koishi +3
We propose a water pump that actively transports water molecules through nanochannels. Spatially asymmetric thermal fluctuations imposed on the channel radius cause unidirectional…
A stochastic Hamiltonian formulation applied to dissipative particle dynamics
Linyu Peng, Noriyoshi Arai, Kenji Yasuoka
In this paper, a stochastic Hamiltonian formulation (SHF) is proposed and applied to dissipative particle dynamics (DPD) simulations. As an extension of Hamiltonian dynamics to sto…
Correlation between ordering and shear thinning in confined liquids
Yusei Kobayashi, Noriyoshi Arai, Kenji Yasuoka
Despite the extensive research that has been conducted for decades on the behavior of confined liquids, detailed knowledge of this phenomenon, particularly in the mixed/boundary lu…
Prediction of transport property via machine learning molecular movements
Ikki Yasuda, Yusei Kobayashi, Katsuhiro Endo +5
Molecular dynamics (MD) simulations are increasingly being combined with machine learning (ML) to predict material properties. The molecular configurations obtained from MD are rep…
Impact of Free Energy of Polymers on Polymorphism of Polymer-Grafted Nanoparticles
Masanari Ishiyama, Kenji Yasuoka, Makoto Asai
We focus on polymer-grafted nanoparticles (PGNP). A PGNP is composed of two different layers: the hard core of a nanoparticle and the soft corona of grafted polymers on the surface…
MD-GAN with multi-particle input: the machine learning of long-time molecular behavior from short-time MD data
Ryo Kawada, Katsuhiro Endo, Daisuke Yuhara +1
MD-GAN is a machine learning-based method that can evolve part of the system at any time step, accelerating the generation of molecular dynamics data. For the accurate prediction o…