33 citations · 59 across the 9 of their papers we have counts for
3 papers · 1 filter
Auto-WHATMD : Automated Wasserstein-based High-dimensional feature extraction Analysis of Trajectories from Molecular Dynamics
Sosuke Asano, Ikki Yasuda, Katsuhiro Endo +2
Comparing multiple protein systems with variation such as different binding ligands or mutations, and understanding their effects is one of the objectives in molecular dynamics sim…
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…