リーウェイ

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リーウェイ
博士研究員
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連絡先

wel208 atmark icredd.hokudai.ac.jp

マイケル・ルビンスタイン グループ
主任研究者
教員
博士研究員
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    リーウェイ

研究紹介

研究テーマ

Rational in-silico design of polymer networks

キーワード

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

研究概要

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. 

代表的な研究成果

  • 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

業績一覧

2022年

  • 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