LI, Wei

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LI, Wei
Postdoctoral Fellow
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Contact

wel208 atmark icredd.hokudai.ac.jp

RUBINSTEIN, Michael Group
Principal Investigator
Faculty Members
Postdoctoral Fellows
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    LI, Wei

About the Research

Research Theme

Rational in-silico design of polymer networks

Keyword

Polymer Physics, Soft Materials, Computational Physics, Molecular Dynamics, Multi-scale Modeling, Machine Learning

Research Outline

My research interests lie in understanding complex systems. I have been working on problems related to polymer physics and soft matter by means of theoretic tools, computational techniques, and numerical methods. My research goals are to advance the state-of-the-art theory and simulation tools in the field and to provide insightful guidance and innovative strategies for functional applications in smart materials, biomedical engineering, and energy storage. My current research focuses on studying mechanical properties of polymer networks through theory and molecular dynamics simulations. 

Representative Research Achievements

  • Self-Consistent Field Theory Study of Polymer-mediated Colloidal Interactions in Solution: Depletion Effects and Induced Forces
    W. Li, K. Delaney, G. Fredrickson. J. Chem. Phys., 2021, 155, 154903.
    DOI: 10.1063/5.0065742
  • Dynamics of Long Entangled Polyisoprene Melts via Multiscale Modeling
    W. Li, P. K. Jana, A. Behbahani, G. Kritikos, L. Schneider, P. Polińska, C. Burkhart, V. Harmandaris, M. Müller, M. Doxastakis. Macromolecules, 2021, 54, 8693–8713.
    DOI: 10.1021/acs.macromol.1c01376
  • Glass Transition of Ion-containing Polymer Melts in Bulk and Thin Films
    W. Li, M. Olvera de la Cruz. Soft Matter, 2021, 17, 8420-8433.
    DOI: 10.1039/D1SM01098K
  • Tailoring Interfacial Properties in Polymer-Silica Nanocomposites via Surface Modification: An Atomistic Simulation Study.
    W. Li, P. Bacova, A. Behbahani, C. Burkhart, P. Polińska, V. Harmandaris, M. Doxastakis. ACS Applied Polymer Materials, 2021, 3, 2576–2587.
    DOI: 10.1021/acsapm.1c00197
  • Backmapping Coarse-grained Macromolecules: An Efficient and Versatile Machine-learning Approach
    W. Li, C. Burkhart, P. Polińska, V. Harmandaris, M. Doxastakis. J. Chem. Phys., 2020, 153, 041101.
    DOI: 10.1063/5.0012320

Publications

2024

  • Tuning Network Structures of Hydrophobic Hydrogels by Controlling Polymerization Solvent
    H. Fan, D. Naohara, W. Li, X. Li, J. P. Gong, Polym. Chem., 2024, ,
    DOI: 10.1039/d4py00256c

2023

  • Molecular Dynamics Simulations of Ideal Living Polymerization: Terminal Model and Kinetic Aspects
    Wei Li, J. Phys. Chem. B, 2023, 127, 35, 7624–7635
    DOI: 10.1021/acs.jpcb.3c03126
  • Polymer Brush Inspired by Ribosomal RNA Transcription
    T. Yamamoto, W. Li, European Physical Journal E, 2023, 46, 61
    DOI: 10.1140/epje/s10189-023-00323-5

2022

  • Deep Convolutional Neural Networks for Generating Atomistic Configurations of Multi-Component Macromolecules from Coarse-Grained Models
    E. Christofi, A. Chazirakis, C. Chrysostomou, M. A. Nicolaou, W. Li, M. Doxastakis, V. A. Harmandaris, J. Chem. Phys., 2022, 157, 184903
    DOI: 10.1063/5.0110322