研究内容

(1) 理論・計算・データ科学手法

• (1-1) Density Functional Theory (DFT)-based molecular dynamics (MD) sampling of electrochemical reactions and ionics
• (1-2) Bias effect for interfaces
• (1-3) Machine learning approaches for disordered systems (ex. heterogeneous interface CALYPSO method)
• (1-4) Advanced MD methods (ex. Non-equilibrium MD)
• (1-5) Revisit the classical electrochemistry as well as ionics theories with DFT viewpoints.

(2) リチウムイオン二次電池・次世代蓄電池

• (2-1) Issues related to the advanced Li-ion batteries: deposition/disolution, dendrite, SEI, concentrated electrolyte, electron and ion transfer at interfaces
• (2-2) Issues related to next-generation batteries: Na-ion, Mg-ion
• (2-3) Issues for solid-state batteries: interfacial electron and ion transfer, coating layer, ionics, ionic correlation
• (2-4) Machine-learning potential and machine-learning approach to the descriptors on ion transport and degradation
• (2-5) For utlization of organic materials for batteries

(3) 触媒・光触媒・太陽電池・燃料電池

• (3-1) Catalytic reactions at oxide surfaces/interfaces (H2O, H2, O2, NH3, N2, CH4, CO2)
• (3-2) Microkinetic analysis of heterogeneous catalysts
• (3-3) Surface/Interface states and ion transport in the photovoltaic perovskite materials

利用プログラム

• 独自のプログラム、プリ・ポストプログラムの作成(Fortran, C, python)
• CPMD, stat-CPMD, CP2K
• VASP
• Quantum Espresso
• Gaussian
• LAMMPS, GROMACS, Amber