
Ikabata, Yasuhiro
| Affiliation | Information and Media Center |
|---|---|
| Concurrent post | Department of Computer Science and Engineering |
| Title | Associate Professor |
| Fields of Research | Computational Chemistry, Chemoinformatics, Computational Science, High-Performance Computing |
| Degree | Doctor of Science (Waseda University) |
| Academic Societies | American Chemical Society, The Chemical Society of Japan, Society of Computer Chemistry, Japan, Japan Society of Theoretical Chemistry, Japan Society for Molecular Science |
| Laboratory website URL | https://cch.cs.tut.ac.jp/ |
| Researcher information URL(researchmap) | Researcher information |
Research
Development of electronic structure theory, algorithms and programs are important to further promote research and development using computational chemistry. I have worked on theories and applications of quantum chemical calculations for solving the Schrödinger equation of molecular systems. Recently, I have been working on several themes: methodology and program development in computational chemistry, research and education on data science related to chemoinformatics and quantum chemistry, and computational study on molecular photoproperties using quantum chemical calculations.
Theme1:Development of Computational Chemistry Methods and Programs
Selected publications and works
"Reconstruction of Four-Body Statistical Pseudopotential for Protein-Peptide Docking", T. Yamamoto, Y. Ikabata, H. Goto, J. Comput. Chem. Jpn. Int. Ed., 10, 2023-0039 (2024).
“Picture-change correction in relativistic density functional theory”, Y. Ikabata, H. Nakai, Phys. Chem. Chem. Phys., 23, 15458 (2021).
"相対論的量子化学計算プログラム RAQET の公開", 五十幡康弘, 吉川武司, 中井浩巳, J. Comput. Chem. Jpn. 18, A6-A11 (2019).
"Local response dispersion method: A density‐dependent dispersion correction for density functional theory", Y Ikabata, H Nakai, Int. J. Quantum Chem., 115, 309-324 (2015).
Theme2:Data Science on Quantum Chemical Calculation
Selected publications and works
"Development of Machine Learning Models to Reproduce Coupled-Cluster Molecular Energies", T. Somego, Y. Ikabata, H. Goto, J. Comput. Chem. Jpn., 24, 95-98 (2026).
"GIKADAI 数理・データサイエンス・AI 教育プログラムとケモインフォマティクス教材の開発", 五十幡康弘, 後藤仁志, 日本化学会情報化学部会誌, 42, 2-5 (2024).
“Machine-learned electron correlation model based on frozen core approximation”, Y. Ikabata, R. Fujisawa, J. Seino, T. Yoshikawa, H. Nakai, J. Chem. Phys. 153, 184108 (2020).
“Machine-learned electron correlation model based on correlation energy density at complete basis set limit”, T. Nudejima, Y. Ikabata, J. Seino, T. Yoshikawa, H. Nakai, J. Chem. Phys. 151, 024104 (2019).
"Semi-local machine-learned kinetic energy density functional with third-order gradients of electron density", J. Seino, R. Kageyama, M. Fujinami, Y. Ikabata, H. Nakai, J. Chem. Phys., 148 241705 (2018).
Theme3:Quantum Chemical Study on Molecular Photoproperties
Selected publications and works
“Direct NIR-Light-Activatable Phthalocyanine Catalysts”, Y. Katsurayama*, Y. Ikabata*, H. Maeda, M. Segi, H. Nakai, T. Furuyama, Chem. Eur. J. 28, e202103223 (2022). (*Equal contributions)
“An Element-Substituted Cyclobutadiene Exhibiting High-Energy Blue Phosphorescence”, Y. Shoji*, Y. Ikabata*, I. Rhyzhii, R. Ayub, O. El Bakouri, T. Sato, Q. Wang, T. Miura, B. S. B. Karunathilaka, Y. Tsuchiya, C. Adachi, H. Ottosson, H. Nakai, T. Ikoma, T. Fukushima, Angew. Chem. Int. Ed. 60, 21817−21823 (2021). (Very Important Paper, *Equal contributions)
“Near-Infrared Absorption of π-Stacking Columns Composed of Trioxotriangulene Neutral Radicals”, Y. Ikabata, Q. Wang, T. Yoshikawa, A. Ueda, T. Murata, K. Kariyazono, M. Moriguchi, H. Okamoto, Y. Morita, H. Nakai, npj Quantum Materials 2, 27 (2017).
“Unveiling a New Aspect of Simple Arylboronic Esters: Long-Lived Room-Temperature Phosphorescence from Heavy-Atom-Free Molecules”, Y. Shoji*, Y. Ikabata*, Q. Wang, D. Nemoto, A. Sakamoto, N. Tanaka, J. Seino, H. Nakai, T. Fukushima, J. Am. Chem. Soc. 139, 2728-2733 (2017). (*Equal contributions)
Title of class
Advances in Computational Simulations, HPC Programming 1, HPC Programming 2, Advanced Data Science Exercise
