Understanding Electron Correlation via Computational Quantum Chemistry

Computational quantum chemistry has become a valuable tool to understand complicated physics arising from the correlated motion of electrons in molecules and materials. In quantum chemistry, we categorize electron correlation mainly into two classes: weak and strong correlation. Weakly correlated electrons can be efficiently handled by perturbation theory (PT). On the other hand, such PT methods completely break down for systems with strong correlation, which then usually requires far more computationally demanding approaches. My thesis addresses multiple aspects of the challenges posed by these two classes of correlation. In this talk, I will present three representative examples from my thesis. First, I will discuss the development of a PT method that can distinguish weak and strong correlation [1]. Secondly, I will present a Kohn-Sham density functional theory approach that can handle some strongly correlated systems by breaking time-reversal symmetry [2]. Lastly, I will discuss the development of a method that can efficiently treat strong spin correlation between electrons and its application to a single molecular magnet [3].
[1] Phys. Chem. Chem. Phys. 2019, 21, 4763–4778.
[2] Phys. Rev. Lett. 2019, 123, 113001.
[3] J. Chem. Phys. 2018, 149, 244121.