Probability density distribution in the 3-dimensional representation of hydrogen several p orbitals (Figure 2), d orbitals (Figure 3), or f orbitals (Figure 5) was developed [6, 7]. It was compared with 3-D isosurface model such as Figure 1 (c) or Figure 4. Isosurface models can hardly show the entire region where an electron can be found. On the other hand, in the diagram of probability density distribution models, an electron is found everywhere around the nucleus. The density of the dots sculptured in the glass block (Figures 2, 3, 5) shows the probability density of finding an electron, so the nodal plane is well described as spherical shell(s) or planar and conical node(s) symmetrical about z axis (Figure 6) where the dots cannot be found. In the diagram of probability density distribution model, we can compare the size of the orbitals from their distribution regions. Number of these nodes together with planar nodes including z axis in hydrogen atomic orbitals is summarized in Table 1 in terms of n: principal quantum number, l: azimuthal quantum number, and m: magnetic quantum number.
DsRed is a sort of red fluorescent protein (RFP) isolated from Discosoma coral. Previously, we successfully calculated the excitation and emission energies of DsRed, based on the CIS (D) type excited state method in conjunction with the multilayer version of fragment molecular orbital (MFMO) scheme [Mochizuki et al., Chem. Phys. Lett., 433, 360 (2007) & Taguchi et al., J. Phys. Chem. B, 113, 1153 (2009) ]. However, there have been no reports on the effects from water molecules, configurations of amino acid residues surrounding the DsRed chromophore. In the present work, we made a series of examinations on these effects on the excitation energy of DsRed. As a result, the following three factors were found to be important in obtaining quantitative correspondence to the experimental value; (1) two water molecules having hydrogen-bonds with the crucial pigment part, (2) orientation of the OH group of Ser69 to make a hydrogen-bond with CRQ66, and (3) configurational relaxation of neighboring amino acid residues (in particular, positively charged Lys163 as well as negatively charged Glu215). The present work could justify the reliability of protein modeling in our previous two papers.
The aqueous lithium-air battery is receiving considerable research attention because of its high theoretical energy density. However, high-power discharge has not been achieved. Because ion transport phenomena in the battery determine current density, molecular interpretation of the electrolyte is required to improve the battery performance. In order to clarify the fundamental mechanism, the present study investigated LiCl electrolyte by using molecular dynamics simulation. The results showed that hydrated structures of Cl- and Li+ were reproduced successfully. Although the LiCl solution is categorized as strong electrolyte, Cl- and Li+ existed with forming ion-pair. Formation of cluster structure was also suggested under higher concentration condition. As a result, the reduction of self-diffusion coefficient caused by the increasing of hydration radius was investigated.
A molecular dynamics (MD) simulation program is developed using Wolfram Mathematica  as a learning guide for beginners. One can simulate particles interacting under the Lennard-Jones 12–6 potential function in a cubic cell with three-dimensional periodic boundary conditions. Microcanonical (NEV) ensemble and canonical (NVT) ensemble are available on the program (see Figure 2). The MD conditions, number of particles, number density, temperature, number of MD steps, and others, are set in a Mathematica notebook ﬁle . Thermodynamic properties, particle trajectories, conﬁgurations of the system, pair correlation function, velocity autocorrelation function, mean square displacement, and self-diffusion coefﬁcient are output as a result of the MD simulation (see Figures 3–7).