Browsing by Author "Shen, Zhengyuan"

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    Molecular Simulation and Design of High-χ Low-N Block Oligomers for Control of Self-Assembly
    (2022-02) Shen, Zhengyuan
    Multi-component oligomer systems are exciting candidates for nanostructured functional materials, due to the wide variety of their self-assembled morphologies with extremely small feature size. However, experimentally screening through the vast design space of molecular architectures can be extremely laborious. Therefore, guidance from predictive modeling is essential to reduce the synthetic effort. This dissertation discusses the predictive design of self-assembling block oligomer systems using molecular simulations, and the development of computer vision models for automated morphology detection for simulation trajectories. Work presented in this thesis creates a roadmap for efficient computational screening of shape-filling molecules, thus accelerating the design and discovery of nanostructured functional materials. First, with the aid of experimentally-validated force fields, molecular dynamics simulations were exploited to design: 1) a series of symmetric triblock oligomers that can self-assemble into ordered nanostructures with sub-1 nm domains and full domain pitches as small as 1.2 nm, 2) Blends of a lamellar-forming diblock oligomer and a cylinder-forming miktoarm star triblock oligomer leading to stable gyroid networks over a large composition window. Similarities and distinctions between the self-assembly phase behavior of these block oligomers and block polymers are discussed. Second, existing simulation data were used to train deep learning models based on three-dimensional point clouds and voxel grids. The pretrained neural networks can readily detect equilibrium morphologies, and also give rich insights of emerging patterns throughout new simulations with different system sizes and molecular dimensions.
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    Supporting Data for "Development of a PointNet for Detecting Morphologies of Self-Assembled Block Oligomers in Atomistic Simulations"
    (2021-08-30) Shen, Zhengyuan; Sun, Yangzesheng; Lodge, Timothy P; Siepmann, J Ilja; siepmann@umn.edu; Siepmann, J Ilja; University of Minnesota MRSEC
    Molecular simulations with atomistic or coarse-grained force fields are a powerful approach for understanding and predicting the self-assembly phase behavior of complex molecules. Amphiphiles, block oligomers, and block polymers can form mesophases with different ordered morphologies describing the spatial distribution of the blocks, but entirely amorphous nature for local packing and chain conformation. Screening block oligomer chemistry and architecture through molecular simulations to find promising candidates for functional materials is aided by effective and straightforward morphology identification techniques. Capturing 3-dimensional periodic structures, such as ordered network morphologies, is hampered by the requirement that the number of molecules in the simulated system and the shape of the periodic simulation box need to be commensurate with those of the resulting network phase. Common strategies for structure identification include structure factors and order parameters, but these fail to identify imperfect structures in simulations with incorrect system sizes. Building upon pioneering work by DeFever et al. [Chem. Sci.2019, 10, 7503–7515] who implemented a PointNet (i.e., a neural network designed for computer vision applications using point clouds) to detect local structure in simulations of single-bead particles and water molecules, we present a PointNet for detection of nonlocal ordered morphologies of complex block oligomers. Our PointNet was trained using atomic coordinates from molecular dynamics simulation trajectories and synthetic point clouds for ordered network morphologies that were absent from previous simulations. In contrast to prior work on simple molecules, we observe that large point clouds with 1000 or more points are needed for the more complex block oligomers. The trained PointNet model achieves an accuracy as high as 0.99 for globally ordered morphologies formed by linear diblock, linear triblock, and 3-arm and 4-arm star-block oligomers, and it also allows for the discovery of emerging ordered patterns from nonequilibrium systems.
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    Supporting Data for "Effects of Electrolytes on Thermodynamics and Structure of Oligo(ethylene oxide)/Salt Solutions and Liquid–Liquid Equilibria of a Squalane/Tetraethylene Glycol Dimethyl Ether Blend"
    (2021-01-22) Shen, Zhengyuan; Chen, Qile P; Lodge, Timothy P; Siepmann, J Ilja; siepmann@umn.edu; Siepmann, J Ilja
    Data including input/output and restart files for all the systems, analysis codes (python, fortran, cpp), and figures in the paper "Effects of Electrolytes on Thermodynamics and Structure of Oligo(ethylene oxide)/Salt Solutions and Liquid–Liquid Equilibria of a Squalane/Tetraethylene Glycol Dimethyl Ether Blend". Sample movie files of the production trajectory are provided.
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    Supporting Data for "From Order to Disorder: Computational Design of Triblock Amphiphiles with 1 nm Domains"
    (2020-07-06) Shen, Zhengyuan; Chen, Jingyi L; Vernadskaia, Viktoriia; Ertem, S Piril; Mahanthappa, Mahesh K; Hillmyer, Marc A; Reineke, Theresa M; Lodge, Timothy P; Siepmann, J Ilja; siepmann@umn.edu; Siepmann, J Ilja; Materials Research Science & Engineering Center (MRSEC)
    Data including input/output and restart files for all the systems, analysis codes (python, fortran, cpp), and figures in the paper "From Order to Disorder: Computational Design of Triblock Amphiphiles with 1 nm Domains." Sample molecular dynamics trajectories pieces are provided due to the extremely long simulation trajectories.