Metal nanoparticles are useful as catalysts having specific reactivity owing to highly reactive site and strong size dependency. Structural information of metal nanoparticles is essential for interpretation and prediction of their reactivity. Wulff theorem predicts the equilibrium structures of crystals by using the surface energies of plane indices such as (111), (110), and (100). In this study, we evaluated the surface energies of well-defined Rh surfaces by the first principles calculations, followed by systematically constructing various sizes of Rh nanoparticles based on the Wulff theorem. For small nanoparticles with radii of 2 nm or less, only the (111) and (100) planes were present. On the other hand, high index surfaces appeared at large nanoparticles, of which the radii were more than 2.5 nm.
We previously proposed an excitation configuration analysis for the divide-and-conquer (DC)-based excited-state calculation method using dynamical polarizability to interpret the nature of excited states [J. Chem. Phys. 160, 244103 (2024)]. This article reviews the proposed DC-based excitation configuration analysis and applies the natural transition orbital analysis based on the (de-)excitation coefficients obtained from the proposed method to the lowest excited state of tris-triphenylacetylammonia. The singular values of excitation coefficients were matched to the results of the TDHF method. Also, the shapes of the natural transition orbitals coincided as well.
To verify the possibility to simulate depolymerization, simulations of the depolymerization of polystyrene were performed. Molecular Dynamics simulations (MD) using neural network potentials were found to be similar in accuracy to MD using density functional theory calculations. It was also found that long-time simulations using neural network potential-MD predicted styrene monomer yields close to those obtained experimentally, and that the monomer yields tended to decrease with increasing pressure.