Liquid flow dynamics through the armchair (6,6)-(160,160) carbon nanotubes (CNTs) is elucidated by molecular dynamics simulations. The liquid is modeled by nonpolar argon atoms to understand the fundamental flow physics. The velocity profiles and slip lengths are discussed considering the radial distributions of the fluid density by the presently proposed finite difference-based velocity fitting method. It is found that as the CNT diameter D increases, the slip length and the flow rate enhancement show three-step transitional profiles in the region of D≤2.3 nm. The slip length and the flow rate stepwise increase at the first transition while they drop at the second and third transitions. The first transition corresponds to the structural change from the single-file chain to single-ring structures of the molecule cluster. The second and third transitions take place when the ring structure starts to develop another inner layer.
It is highly desirable but difficult to understand how microscopic molecular details influence the macroscopic material properties, especially for soft materials with complex molecular architectures. In this study we focus on liquid crystal elastomers (LCEs) and aim at identifying the design variables of their molecular architectures that govern their macroscopic deformations. We apply the regression analysis using machine learning (ML) to a database containing the results of coarse grained molecular dynamics simulations of LCEs with various molecular architectures. The predictive performance of a surrogate model generated by the regression analysis is also tested. The database contains design variables for LCE molecular architectures, system and simulation conditions, and stress–strain curves for each LCE molecular system. Regression analysis is applied using the stress–strain curves as objective variables and the other factors as explanatory variables. The results reveal several descriptors governing the stress–strain curves. To test the predictive performance of the surrogate model, stress–strain curves are predicted for LCE molecular architectures that were not used in the ML scheme. The predicted curves capture the characteristics of the results obtained from molecular dynamics simulations. Therefore, the ML scheme has great potential to accelerate LCE material exploration by detecting the key design variables in the molecular architecture and predicting the LCE deformations.
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