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<p>This review explores molecular dynamics simulations for studying radiation damage in Tritium Producing Burnable Absorber Rod (TPBAR) materials, emphasizing the role of interatomic potentials in displacement cascades. Recent machine learning potentials (MLPs), trained on quantum data, enhance prediction accuracy over traditional models like EAM. We highlight temperature, PKA energy, and composition effects on damage evolution in TPBAR components, recommending suitable potentials and discussing advancements for materials in extreme radiation environments.</p>

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A review of displacement cascade simulations using molecular dynamics emphasizing interatomic potentials for TPBAR components

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年1月2日

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  • Ankit Roy,
  • Giridhar Nandipati,
  • Andrew M. Casella,
  • David J. Senor,
  • Ram Devanathan,
  • Ayoub Soulami

摘要

This review explores molecular dynamics simulations for studying radiation damage in Tritium Producing Burnable Absorber Rod (TPBAR) materials, emphasizing the role of interatomic potentials in displacement cascades. Recent machine learning potentials (MLPs), trained on quantum data, enhance prediction accuracy over traditional models like EAM. We highlight temperature, PKA energy, and composition effects on damage evolution in TPBAR components, recommending suitable potentials and discussing advancements for materials in extreme radiation environments.