Dr. Ono gave an invited talk at The 57th Annual Meeting of the Biophysical Society of Japan.
Date: September 24 (Tue) – 26 (Thu), 2019
Venue: Seagaia Convention Center, Miyazaki
Authors: Junichi Ono, Chika Okada, Yoshifumi Nishimura, Hiromi Nakai
Title: Clarification of proton transfer reactions in photoreceptive proteins using large-scale quantum molecular dynamics simulations
Symposium: Challenges to get insight into unsolved problems of dynamic response in proteins
Our group member gave 4 oral and 7 poster presentations at the Ninth Conference of the Asia-Pacific Association of Theoretical and Computational Chemists (APATCC 2019).
Date:September 30 ~ October 3, 2019
- ○Hiromi Nakai, “How Can Artificial Intelligence Help Quantum Chemists?”, invited talk.
- ○Yasuhiro Ikabata, Takuro Nudejima, Junji Seino, Takeshi Yoshikawa, Hiromi Nakai, “Machine-learned electron correlation model for accurate reproduction of correlation energy at the basis-set limit”, IC065/Invited Communication, 10/3 12:10-12:20.
- ○A. W. Sakti,C.-P. Chou and H. Nakai, “Density-Functional Tight-Binding Metadynamics Study of Oxy-Carbon Diffusion on (100)-γ-Al2O3 Surface”, Invited Communication.
- ○Junichi Ono, Chika Okada, Yoshifumi Nishimura, Hiromi Nakai, “Large-scale quantum-mechanical molecular dynamics simulations for the long-distance proton transfer in bacteriorhodopsin”, IC027/Invited Communication, 10/01 17:10-17:20.
- ○Junji Seino, Ryo Kageyama, Mikito Fujinami, Yasuhiro Ikabata, Hiromi Nakai, “Semi-local machine-learned kinetic energy density functional for orbital-free density functional theory”, poster.
- ○Takeshi Yoshikawa, Toshiki Doi, Hiromi Nakai, “Linear-scaling divide-and-conquer finite-temperature self-consistent field for static correlation systems”, poster.
- ○C.-P. Chou, A. W. Sakti and H. Nakai, “Recent Development of Automatized Density-Functional Tight-Binding Parameterization for Metal-Containing Systems”, poster.
- ○Mikito Fujinami, Hiroki Maekawara, Junji Seino, Hiromi Nakai, “Machine learning study for optimization of reaction conditions including discrete variables with small number of experiments”, poster.
- ○Hiroki Uratani, Chien-Pin Chou, Hiromi Nakai, “Large-scale quantum-mechanical molecular dynamics simulations of polaron formation process in a lead halide perovskite material using divide-and-conquer type density-functional tight-binding method”, poster.
- ○Mayu Inamori, Yasuhiro Ikabata, Takeshi Yoshikawa, Hiromi Nakai, “Key factor of S0/S1 minimum energy conical intersection”, poster.
- ○Nana Komoto, Takeshi Yoshikawa, Junichi Ono, Yoshifumi Nishimura, Hiromi Nakai, “Practical excited-state simulation of thousands of atoms”, poster.
The paper of “experimental condition optimization by machine learning” jointly conducted with Prof. Takeaki Iwamoto laboratory (Tohoku University) was adopted in Inside Cover Image of Chem. Lett.
“Virtual reaction condition optimization based on machine learning for a small number of experiments in high-dimensional continuous and discrete variables”
M. Fujinami, J. Seino, T. Nukazawa, S. Ishida, T. Iwamoto, H. Nakai, Chem. Lett., 48 (8), 961-964 (2019). (Editor’s Choice)