no code implementations • 7 Mar 2022 • Ikki Yasuda, Yusei Kobayashi, Katsuhiro Endo, Yoshihiro Hayakawa, Kazuhiko Fujiwara, Kuniaki Yajima, Noriyoshi Arai, Kenji Yasuoka
Molecular dynamics (MD) simulations are increasingly being combined with machine learning (ML) to predict material properties.
no code implementations • 2 Feb 2022 • Ryo Kawada, Katsuhiro Endo, Daisuke Yuhara, Kenji Yasuoka
Therefore, the selection of the part of the system is important for efficient learning.
no code implementations • 3 Sep 2021 • Ikki Yasuda, Katsuhiro Endo, Eiji Yamamoto, Yoshinori Hirano, Kenji Yasuoka
Prediction of protein-ligand binding affinity is a major goal in drug discovery.
1 code implementation • 28 May 2020 • Katsuhiro Endo, Taichi Nakamura, Keisuke Fujii, Naoki Yamamoto
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 distribution for approximating a given hard-to-sample distribution.
Quantum Physics