As a more economical but similarly accurate computation method than the Ewald sum, the isotropic periodic sum (IPS) method for nonpolar molecules (IPSn) and polar molecules (IPSp), along with the Wolf method are of interest, but the cutoff radius dependence is an important issue. To evaluate the cutoff radius effect of the three methods, a water-vapor interfacial system has been studied by molecular dynamics. The Wolf method can produce adequate results for surface tension compared to that of the Ewald sum (within 2.9%) at a long enough cutoff radius, rc. However, the estimation of the electrostatic potential profile and dipole orientational function is poor. The Wolf method cannot estimate electrostatic configuration at rc ⩽ Lz/2 (Lz is the longest lattice of the system). We have found that the convergence of the surface tension and the electrostatic configuration of the IPSn method is faster than that of the IPSp method. Moreover, the IPSn method is most accurate among the three methods for the same cutoff radius. Furthermore, the behavior of the surface tension against the cutoff radius shows a greater difference for the IPSn and IPSp method. The surface tension of the IPSp method fluctuates and presents a similar result to that of the Ewald sum, but the surface tension for the IPSn method greatly deviates near rc = Lz/3. The cause of this deviation is the difference between the interfacial configuration of the water surface and the cutoff treatment of the IPS method. The deviation becomes insignificant far from rc = Lz/3. In spite of this shortcoming, the IPSn method gives the most accurate result in estimating the surface tension at rc = Lz/2. From all the results in this work, the IPSn and IPSp method have been found to be more accurate than the Wolf method. In conclusion, the surface tension and structure of water-vapor interface can be calculated by the IPSn method when rc is greater than or equal to the longest lattice of the system. The IPSp method and the Wolf method require a longer cutoff radius than the longest lattice of the system to estimate interfacial properties.

Molecular dynamics simulation has been applied for water to compare the isotropic periodic sum (IPS) method [X. Wu and B. R. Brooks, J. Chem. Phys. 122, 044107 (2005)] with the Ewald sum based on the diffusion coefficient and liquid structure. The IPS method gives a good estimation for the self-diffusion coefficient at a cutoff radius, rc, greater than 2.2 nm; however, the radial distribution function g(r) has a notable deviation. The peak of this deviation appears at specific intermolecular distances which are near each cutoff radius and decrease in proportion to the inverse of the cube of rc. Thus the deviation becomes insignificant (less than 1%) at rc greater than 2.2 nm. The distance dependent Kirkwood factor Gk(r) was also calculated, and since the truncation of a long-range interaction of the cutofflike method (such as cutoff with or without the switch function and the reaction field) shows serious shortcomings for dipole-dipole correlations in bulk water systems, this was observed by comparing the shape to that of the Ewald sum [Y. Yonetani, J. Chem. Phys. 124, 204501 (2006); D. van der Spoel and P. J. van Maaren, J. Chem. Theory Comput. 2, 1 (2006)]. The Gk(r) of cutofflike method greatly deviate from that of the Ewald sum. However, the discrepancy of Gk(r) for the IPS method was found to be much less than that of other typical cutofflike methods. In conclusion, the IPS method is an adequately accurate technique for estimating transport coefficients and the liquid structure of water in a homogeneous system at long cutoff distances.

Phase transition of anisotropic materials is ubiquitously observed in physics, biology, materials science, and engineering. Nevertheless, how anisotropy of constituent molecules affects the phase transition dynamics is still poorly understood. Here we investigate numerically the phase transition of a simple model system composed of anisotropic molecules, and report on our discovery of multistep nucleation of nuclei with layered positional ordering (smectic ordering), from a fluid-like nematic phase with orientational order only (no positional order). A trinity of molecular dynamics simulation, machine learning, and molecular cluster analysis yielding free energy landscapes unambiguously demonstrates the dynamics of multistep nucleation process involving characteristic metastable clusters that precede supercritical smectic nuclei and cannot be accounted for by the classical nucleation theory. Our work suggests that molecules of simple shape can exhibit rich and complex nucleation processes, and our numerical approach will provide deeper understanding of phase transitions and resulting structures in anisotropic materials such as biological systems and functional materials.

Molecular dynamics simulations of a Lennard-Jones (LJ) liquid were applied to compare the isotropic periodic sum (IPS) method [X. Wu and B. R. Brooks, J. Chem. Phys. 122, 044107 (2005)], which can reduce the calculation cost of long-range interactions, such as the Lennard-Jones and Coulombic ones, with the cutoff method for the transport coefficients which includes the self-diffusion coefficient, bulk viscosity, and thermal conductivity. The self-diffusion coefficient, bulk viscosity, and thermal conductivity were estimated with reasonable accuracy if the cutoff distance of the LJ potential for the IPS method was greater than 3σ. The IPS method is an effective technique for estimating the transport coefficients of the Lennard-Jones liquid in a homogeneous system.

Elucidation of mesoscopic structures of molecular systems is of considerable scientific and technological interest for the development and optimization of advanced materials. Molecular dynamics simulations are a promising means of revealing macroscopic physical properties of materials from a microscopic viewpoint, but analysis of the resulting complex mesoscopic structures from microscopic information is a non-trivial and challenging task. In this study, a Machine Learning-aided Local Structure Analyzer (ML-LSA) is developed to classify the complex local mesoscopic structures of molecules that have not only simple atomistic group units but also rigid anisotropic functional groups such as mesogens. The proposed ML-LSA is applied to classifying the local structures of liquid crystal polymer (LCP) systems, which are of considerable scientific and technological interest because of their potential for sensors and soft actuators. A machine learning (ML) model is constructed from small, and thus computationally less costly, monodomain LCP trajectories. The ML model can distinguish nematic- and smectic-like monodomain structures with high accuracy. The ML-LSA is applied to large, complex quenched LCP structures, and the complex local structures are successfully classified as either nematic- or smectic-like. Furthermore, the results of the ML-LSA suggest the best order parameter for distinguishing the two mesogenic structures. Our ML model enables automatic and systematic analysis of the mesogenic structures without prior knowledge, and thus can overcome the difficulty of manually determining the specific order parameter required for the classification of complex structures.

Long-timescale molecular dynamics simulations were performed to estimate the actual physical nature of a united-atom model of polyethylene (PE). Several scaling laws for representative polymer properties are compared to theoretical predictions. Internal structure results indicate a clear departure from theoretical predictions that assume ideal chain statics. Chain motion deviates from predictions that assume ideal motion of short chains. With regard to linear viscoelasticity, the presence or absence of entanglements strongly affects the duration of the theoretical behavior. Overall, the results indicate that Gaussian statics and dynamics are not necessarily established for real atomistic models of PE. Moreover, the actual physical nature should be carefully considered when using atomistic models for applications that expect typical polymer behaviors.