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<p>In this work, a multi-stage material design framework combining machine learning techniques with density functional theory is established to reveal the mechanism of phase stabilization in HfO<sub>2</sub> based ferroelectric materials. The ferroelectric phase fractions based on a more stringent relationship of phase energy differences is proposed as an evaluation criterion for the ferroelectric performance of hafnium-based materials. Based on the Boltzmann distribution theory, the abstract phase energy difference is converted into an intuitive phase fraction distribution mapping. A large-scale prediction of unknown dopants is conducted within the material design framework, and gallium (Ga) is identified as a new dopant for HfO<sub>2</sub>. Both experiments and density functional theory calculations demonstrate that Ga is an excellent dopant for ferroelectric hafnium oxide, especially, the experimentally determined variation trends of ferroelectric phase fraction and polarization properties with Ga doping concentration are in good agreement with the predictions given by machine learning. This work provides a new perspective from machine learning to deepen the understanding of the ferroelectric properties of HfO<sub>2</sub> materials, offering fresh insights into the design and performance prediction of HfO<sub>2</sub> ferroelectric thin films.</p>

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Artificial intelligence-driven phase stability evaluation and new dopants identification of hafnium oxide-based ferroelectric materials

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年1月5日

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  • Shaoan Yan,
  • Pei Xu,
  • Gang Li,
  • Yuchun Li,
  • Yingfang Zhu,
  • Xiaona Zhu,
  • Qiong Yang,
  • Meng Li,
  • Minghua Tang,
  • Hongliang Lu,
  • Sen Liu,
  • Qingjiang Li,
  • David Wei Zhang,
  • Zhigang Chen

摘要

In this work, a multi-stage material design framework combining machine learning techniques with density functional theory is established to reveal the mechanism of phase stabilization in HfO2 based ferroelectric materials. The ferroelectric phase fractions based on a more stringent relationship of phase energy differences is proposed as an evaluation criterion for the ferroelectric performance of hafnium-based materials. Based on the Boltzmann distribution theory, the abstract phase energy difference is converted into an intuitive phase fraction distribution mapping. A large-scale prediction of unknown dopants is conducted within the material design framework, and gallium (Ga) is identified as a new dopant for HfO2. Both experiments and density functional theory calculations demonstrate that Ga is an excellent dopant for ferroelectric hafnium oxide, especially, the experimentally determined variation trends of ferroelectric phase fraction and polarization properties with Ga doping concentration are in good agreement with the predictions given by machine learning. This work provides a new perspective from machine learning to deepen the understanding of the ferroelectric properties of HfO2 materials, offering fresh insights into the design and performance prediction of HfO2 ferroelectric thin films.