Researcher Information


Associate Professor

Elucidating molecules and reactions with computational chemistry and data science

Department of Chemistry, Physical Chemistry


Development of accurate electronic structure methods for large molecules / Reaction analysis and prediction with data science

FieldTheoretical Chemistry, Quantum Chemistry, Electronic Structure Theory, Computational Chemistry, Data Science
KeywordLarge-Scale Calculation, Electron Correlation, Massively Parallel Computation, K-Computer, Divide-and-Conquer Method, Catalyst Informatics, Quantum Molecular Dynamics

Introduction of Research

Our goal is to predict structure, properties, and reactions of materials with computer.
Every material consists of electrons and nuclei. Very small substance such as electron behaves according to "quantum mechanics." If we can solve the Schroedinger equation, which is the basic equation of the quantum mechanics, on computers, we can achieve our goal! But, it is not so easy. The number of electrons tructable in a calculation is limited because even the simplest method requires the computational time proportional to the 3rd power of the number of electrons. We are tackling to the prediction of the behavior of huge molecules (over 1 million atom in some cases) by using the divide-and-conquer (DC) method, which is an advanced computational technique, and massively parallel computers like Fugaku computer.
It is, however, still difficult to describe the real chemical world involving Avogadro number atoms. So, we are importing "informatics" and/or "artificial intelligence" and are trying to elucidate and predict the catalytic reaction mechanisms and efficiency.

Representative Achievements

Theoretical and Experimental Studies on the Near‐Infrared Photoreaction Mechanism of a Silicon Phthalocyanine Photoimmunotherapy Dye: Photoinduced Hydrolysis by Radical Anion Generation,
M. Kobayashi, M. Harada, H. Takakura, K. Ando, Y. Goto, T. Tsuneda, M. Ogawa, and T. Taketsugu, ChemPlusChem, 2020, 85, 1959-1963. (Front Cover Article)
Surface Adsorption Model Calculation Database and Its Application to Activity Prediction of Heterogeneous Catalysts,
M. Kobayashi, H. Onoda, Y. Kuroda, and T. Taketsugu, J. Comput. Chem. Jpn., 2019, 18, 251-253.
Automated Error Control in Divide-and-Conquer Self-Consistent Field Calculations,
M. Kobayashi, T. Fujimori, and T. Taketsugu, J. Comput. Chem., 2018, 38, 909-916. (Cover Article)
Three Pillars for Realizing Quantum Mechanical Molecular Dynamics Simulations of Huge Systems: Divide-and-Conquer, Density Functional Tight-Binding, and Massively Parallel Computation,
H. Nishizawa, Y. Nishimura, M. Kobayashi, S. Irle, and H. Nakai, J. Comput. Chem., 2016, 37, 1983-1992.
Alternative Linear-Scaling Methodology for the Second-Order Møller-Plesset Perturbation Calculation Based on the Divide-and-Conquer Method,
M. Kobayashi, Y. Imamura, and H. Nakai, J. Chem. Phys., 2007, 127, 074103.

Related industries

Chemistry, Pharmaceutical Chemistry, Catalysis
Academic degreePh.D. in Science
Academic background2003 B.S. (Waseda University)
2004 M.S. (Waseda University)
2007 Ph.D. in Science (Waseda University)
2006-2008 JSPS Fellow
2008-2012 Visiting Lecturer, Waseda University
2012-2014 Assistant Prof., Waseda Institute for Advanced Study
2014-2017 Assistant Prof., Hokkaido University
2017-2020 Lecturer, Hokkaido University
2020- Associate Prof., Hokkaido University
Affiliated academic societyJapan Society of Theoretical Chemistry, Japan Society of Molecular Science, Society of Computer Chemistry, Japan, The Chemical Society of Japan, Catalysis Society of Japan
ProjectPRESTO-JST "Materials Informatics"
Room addressScience Building 7 7-504