Machine learning-aided analysis for complex local structure of liquid crystal polymers

Abstract

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.

Characterising soft matter using machine learning

“…Existing review articles introduce machine learning 4,5 and cover topics such as drug discovery, 6 multiscale design, 7,8 active matter, 9 fluid mechanics, 10 and chemical engineering. 11 I have chosen a handful of example cases, hence unfortunately I miss a great deal of the existing literature, for example, on amyloid assembly, [12][13][14] analysis of image data, [15][16][17] density functional theory, 18,19 drying blood, 20 liquid crystals, [21][22][23][24][25][26] modeling differential equations [27][28][29] nanoparticle assembly, 30,31 network aging, 32 optimising microscopy, 33 polymers, [34][35][36][37][38][39][40][41] speeding up simulations 42,43 and 3d printing. [44][45][46] Machine learning has a reputation for being applied in haste with too little follow-up.…”

Searching for local order parameters to classify water structures at triple points

“…Well‐designed parameters can be used for tracking time‐dependent local structures and reaction coordinates during structural transitions. Many order parameters have been developed for diverse structures, such as body‐centered cubic, face‐centered cubic, hexagonal close‐packed, smectic, nematic, and liquids 11–21 . Order parameters for water have been used to distinguish liquid, ice Ih, and ice Ic structures 22–25 .…”

“…Machine learning (ML) is promising means for automatically and systematically performing exhaustive searches. Supervised ML has searched over 1 million order parameters, and it has provided the best (set of) order parameter(s) to classify the structures of various Lennard‐Jones (LJ), 28 water, 25 liquid crystal, 29 and liquid‐crystal polymer phases 21 . The order parameters selected for a liquid crystal and its polymers are completely different from conventional orientational order parameters and exhibit much better classification accuracy 21,29 .…”

“…Supervised ML has searched over 1 million order parameters, and it has provided the best (set of) order parameter(s) to classify the structures of various Lennard‐Jones (LJ), 28 water, 25 liquid crystal, 29 and liquid‐crystal polymer phases 21 . The order parameters selected for a liquid crystal and its polymers are completely different from conventional orientational order parameters and exhibit much better classification accuracy 21,29 . Other studies applying ML to structural analyses have demonstrated its great potential for automatic classification and discovery of molecular structures 30–34 …”

“…A total of 159,767,496 combinations of parameters are considered with the aid of supervised ML. The classification accuracy of each order parameter is automatically and systematically determined with a ML‐aided local structure analyzer (ML‐LSA) 21,28,29 . For each triple point, the best set of two order parameters was found that distinguished three structures with high accuracy.…”