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2021 Fiscal Year Research-status Report

Metallo-Dielectric Janus Particles as Building Blocks for Designer Active Materials

Research Project

Project/Area Number 20K03786
Research InstitutionKyoto University

Principal Investigator

MOLINA JOHN  京都大学, 工学研究科, 助教 (20727581)

Project Period (FY) 2020-04-01 – 2023-03-31
KeywordsActive Particles / Janus Particles / Electrophoresis / Quincke Rollers / Machine Learning / Multi-Scale Simulations / Optimal Control
Outline of Annual Research Achievements

Working with other members of our lab, we have studied the collective motion of an active colloidal particle that is closely related to the ICEP Janus Particle: the Quincke rollers. Using a simple model for the propulsion, which assumed a fixed torque, but fully accounting for the hydrodynamics and the induced dipole-dipole interactions, we could better understand the rich behavior observed experimentally, and the crucial role played by the near-field hydrodynamic interactions.

We have also investigated the use of Machine Learning as (1) a probabilistic flow solver, (2) a way to learn constitutive relations, and (3) to tackle inverse optimal control problems. The first can help to interpret experiments on active particles, the second to develop continuum models, and the third to design optimal control strategies. For topic (1) we have developed a Bayesian Stokes flow solver that allows us to infer the velocity/pressure fields given only knowledge of the boundary values (in collaboration with an undergraduate student). Topic (2) was originally developed to accelerate Multi-scale simulations of polymer melts, and has now been extended to interacting/entangled polymer models (in collaboration with a master student). Topic (3) comes from a collaboration to understand the optimal government intervention strategy in an epidemic. For this, we have developed a physics informed neural network (Nash Neural Network) to infer the hidden utility of rational individuals from their behavior. We expect this will have broad applications in science, engineering, and biology.

Current Status of Research Progress
Current Status of Research Progress

3: Progress in research has been slightly delayed.

Reason

Our study on the ICEP Janus particles, and the detailed mechanism behind the velocity reversal is still ongoing, as we have not yet obtained good quantitative agreement with experiments. Even though we can reproduce the velocity reversal, the dependence on the salt concentration is not consistent with experiments. To address these issues we need to further validate and improve our model.
However, we note that we have opened up new promising avenues of research, leveraging machine learning techniques, which can help in our goal of achieving designer active matter.

Strategy for Future Research Activity

We will continue to develop our Janus model, in order to achieve quantitative agreement with experiments. This includes finding a good enough model for the frequency dependent material properties. For this, we will leverage more detailed microscopic models that have been developed to describe these ICEP Janus particles. We will also finalize the Janus functionality into the latest Kapsel code, which now includes support for phase-separating fluids.

In addition, we will pursue the additional research avenues discussed above, in order to leverage the power of machine learning techniques to tackle such complex soft matter problems. This includes developing novel flow solvers (to help analyze experimental results), solve optimal control problems, and infer constitutive relations from microscopic/particle based data, in order to develop accurate coarse-grained models of active matter systems.

Causes of Carryover

The ongoing COVID-19 pandemic has continued to restrict our ability to travel, with most conferences we attended this year being held online. We thought that domestic travel would have returned to normal this past year, but this was also not the case. Going forward, we plan to use these funds to cover super-computing services, conference participation fees, publication fees, and if the situation allows, domestic and international travel costs.

  • Research Products

    (13 results)

All 2022 2021

All Journal Article (1 results) (of which Open Access: 1 results) Presentation (12 results) (of which Int'l Joint Research: 7 results,  Invited: 1 results)

  • [Journal Article] Machine Learning for the Flow Prediction of Fluids with Memory Effects on the Stress2021

    • Author(s)
      TANIGUCHI Takashi、MOLINA John J.
    • Journal Title

      JAPANESE JOURNAL OF MULTIPHASE FLOW

      Volume: 35 Pages: 426~436

    • DOI

      10.3811/jjmf.2021.t008

    • Open Access
  • [Presentation] Collective Dynamics of Quincke Rollers with Fully Resolved Hydrodynamics2022

    • Author(s)
      Shun Imamura (*), Kohei Sawaki, John J. Molina, Ryoichi Yamamoto
    • Organizer
      American Physical Society (APS) March Meeting 2022
    • Int'l Joint Research
  • [Presentation] Application of Machine-learned constitutive relations for well-entangled polymer melt flows2022

    • Author(s)
      Souta Miyamoto (*), John J. Molina, Takashi Taniguchi
    • Organizer
      American Physical Society (APS) March Meeting 2022
    • Int'l Joint Research
  • [Presentation] Rational policy design for epidemics2022

    • Author(s)
      Simon K. Schnyder (*), John J. Molina, Ryoichi Yamamoto, Matthew S. Turner
    • Organizer
      American Physical Society (APS) March Meeting 2022
    • Int'l Joint Research
  • [Presentation] Nash Neural Networks2022

    • Author(s)
      John J. Molina (*), Simon K. Schnyder, Matthew S. Turner, Ryoichi Yamamoto
    • Organizer
      American Physical Society (APS) March Meeting 2022
    • Int'l Joint Research
  • [Presentation] Rational policy design for epidemics2021

    • Author(s)
      Simon K. Schnyder (*), John J. Molina, Ryoichi Yamamoto, Matthew S. Turner
    • Organizer
      27th International Conference on Computing in Economics and Finance
    • Int'l Joint Research
  • [Presentation] Rational policy design for epidemics2021

    • Author(s)
      Simon K. Schnyder (*), John J. Molina, Ryoichi Yamamoto, Matthew S. Turner
    • Organizer
      24.5th Workshop on Economics with Heterogeneous Interacting Agents
    • Int'l Joint Research
  • [Presentation] Learning the constitutive relation of polymer melt flows2021

    • Author(s)
      John J. Molina (*), Souta Miyamoto, Takashi Taniguchi
    • Organizer
      The 2021 International Chemical Congress of Pacific Basin Societies (Pacifichem 2021)
    • Int'l Joint Research
  • [Presentation] 学習された構成関係を用いた高分子液体の流動シミュレーション2021

    • Author(s)
      Souta Miyamoto (*), John J. Molina, Takashi Taniguchi
    • Organizer
      化学工学会 第52回秋季大会(2021)
  • [Presentation] Well-entangled polymer melt flow simulations using a Machine-Learned constitutive relation2021

    • Author(s)
      Souta Miyamoto (*), John J. Molina, Takashi Taniguchi
    • Organizer
      第69回レオロジー討論会
  • [Presentation] A Machine Learning Approach to Flow Problems2021

    • Author(s)
      John J. Molina (*), Takashi Taniguchi
    • Organizer
      第69回レオロジー討論会
  • [Presentation] 機械学習モデルを構成関係に代用した高分子溶融体の流動シミュレーション2021

    • Author(s)
      Souta Miyamoto (*), John J. Molina, Takashi Taniguchi
    • Organizer
      プラスチック成形加工学会第29回秋季大会
  • [Presentation] Gaussian Processes for Machine Learning of Fluid Flows2021

    • Author(s)
      John J. Molina (*), Takashi Taniguchi
    • Organizer
      ソフトバイオ研究会2021
    • Invited

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Published: 2022-12-28  

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