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This study presents an in-depth investigation combining theoretical predictions and experimental measurements to examine how ultraviolet radiation induces structural changes in acrylated epoxidized soybean oil (AESO). The investigation reveals that UV exposure initiates a free-radical-mediated polymerization reaction, which generates heat as new polymer network bonds form. When exposed to an electric field, the material exhibits distinctive dielectric relaxation behavior, attributed to the presence of orientation-dependent defects within the polymer’s local structure. We quantified these defects’ influence through entropic analysis of time-resolved dielectric constant measurements. Additionally, we employed contemporary computational materials science techniques, specifically machine learning interatomic potentials, to model both the energetic parameters and structural characteristics of the system.

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Machine Learning Interatomic Potentials and Orientational Defects in Polymers

  • 会议论文

  • 发表日期：

    2025年

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  • Arnold Kiv,
  • Volodymyr Soloviev,
  • Andrii Tuzhykov,
  • Taras Kavetskyy,
  • Nataliia Hoivanovych,
  • Ondrej Šauša,
  • Jaroslav Rusnák,
  • Helena Švajdlenková,
  • Jolita Ostrauskaite,
  • Andrii Bielinskyi,
  • Mykola Slusarenko

摘要

This study presents an in-depth investigation combining theoretical predictions and experimental measurements to examine how ultraviolet radiation induces structural changes in acrylated epoxidized soybean oil (AESO). The investigation reveals that UV exposure initiates a free-radical-mediated polymerization reaction, which generates heat as new polymer network bonds form. When exposed to an electric field, the material exhibits distinctive dielectric relaxation behavior, attributed to the presence of orientation-dependent defects within the polymer’s local structure. We quantified these defects’ influence through entropic analysis of time-resolved dielectric constant measurements. Additionally, we employed contemporary computational materials science techniques, specifically machine learning interatomic potentials, to model both the energetic parameters and structural characteristics of the system.