1 citations · 1 across the 3 of their papers we have counts for
4 papers
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…
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…
Quantum self-learning Monte Carlo with quantum Fourier transform sampler
Katsuhiro Endo, Taichi Nakamura, Keisuke Fujii +1
The self-learning Metropolis-Hastings algorithm is a powerful Monte Carlo method that, with the help of machine learning, adaptively generates an easy-to-sample probability distrib…
Molecular Flow Monte Carlo
Katsuhiro Endo, Daisuke Yuhara, Kenji Yasuoka
In this paper, we suggest a novel sampling method for Monte Carlo molecular simulations. In order to perform efficient sampling of molecular systems, it is advantageous to avoid ex…