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.

The mesoscopic structures of polymer electrolyte membrane (PEM) affect the performances of fuel cells.

In the analyses of miscibility behaviors of macromolecules and polymers, dissipative particle dynamics (DPD) simulations are generally performed. In these simulations, the so-called χ parameters describing the effective interactions among particles are crucial. It has been known that such parameters can be obtained within the classical or empirical force field frameworks. However, there is a potential problem that charge transfer and polarization occasionally occur. Additionally, satisfactory reference parameters are not available for some cases. Therefore, we developed a new procedure to evaluate the set of parameters by using the ab initio fragment molecular orbital (FMO) method which can provide the set of interaction energies among segments as polymer units. Moreover, we evaluated the anisotropy of molecules by using the FMO-based effective interaction parameters for three standard binary mixture systems (hexane-nitrobenzene, polyisobutylene-diisobutyl ketone, and polyisoprene-polystyrene). The calculated values showed good agreement with the experimental values with about 10% errors.

No abstract

Determining local structures of molecular systems helps the scientific and technological understanding of the function of materials. Molecular simulations provide microscopic information on molecular systems, but analyzing the resulting local structures is a non-trivial task. Many kinds of order parameters have been developed for detecting such local structures. Bond-orientational order parameters are promising for classifying local structures and have been used to analyze systems with such structures as body-centered cubic, face-centered cubic, hexagonal close-packed, and liquid. A specific set of order parameters derived from Lechner’s definitional equation are widely used to classify complex local structures. However, there has been no thorough investigation of the classification capability of other Lechner parameters, despite their potential to precisely distinguish local structures. In this work, we evaluate the classification capability of 112 species of bond-orientational order parameters including Lechner’s definitions. A total of 234 248 combinations of these parameters are also evaluated. The evaluation is systematically and automatically performed using machine learning techniques. To distinguish the four types of local structures, we determine the better set of two order parameters by comparing with a conventional set. A set of three order parameters is also suggested for better accuracy. Therefore, the machine learning scheme in the present study enables the systematic, accurate, and automatic mining of effective order parameters for classifying crystal structures.