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Experiment-in-loop interactive optimization of polymer composites for "5G-and-beyond" communication technologies.

Bin Xu1,2, Touchy Abeda Sultana2, Koki Kitai2

  • 1Department of Mechanical Engineering, The University of Tokyo, 7-3-1 Hongo, Bunkyo, Tokyo, 113-8656, Japan. shiomi@photon.t.u-tokyo.ac.jp.

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Advanced Bayesian optimization accelerates the development of polymer composites for 5G+ communications. This method optimizes materials with low thermal expansion and dielectric loss, crucial for high-frequency applications.

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Area of Science:

  • Materials Science
  • Chemical Engineering
  • Communication Technology

Background:

  • Fifth-generation-and-beyond (5G+) communication technologies require advanced polymer composites.
  • Key material properties include low coefficient of thermal expansion (CTE) and low dielectric loss at high frequencies.
  • Fabrication complexity and parameter knowledge gaps hinder material development.

Purpose of the Study:

  • To develop and apply experiment-in-loop Bayesian optimization (EiL-BO) for optimizing polymer composite materials.
  • To address challenges in high-dimensional parameter spaces for material fabrication.
  • To achieve superior material properties for advanced communication applications.

Main Methods:

  • Utilized state-of-the-art EiL-BO with a Gaussian process and automatic relevance determination kernel.
  • Optimized a perfluoroalkoxyalkane matrix composite with silica fillers.
  • Managed eight-dimensional parameters including filler morphology, surface chemistry, and processing conditions.

Main Results:

  • Achieved a low CTE of 24.7 ppm K⁻¹.
  • Obtained an extinction coefficient of 9.5 × 10⁻⁴.
  • The optimized composite outperformed existing polymeric materials.

Conclusions:

  • EiL-BO is highly effective and versatile for accelerating advanced material development.
  • The optimized composite meets the demanding requirements for 5G+ communication technologies.
  • This approach overcomes challenges in complex, anisotropic multidimensional optimization spaces.