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在无形GeSe中揭示缺陷图案,使用机器学习的原子间潜力

Minseok Moon1, Seungwoo Hwang1, Jaesun Kim1

  • 1Department of Materials Science and Engineering and Research Institute of Advanced Materials, Seoul National University, Seoul 08826, Korea.

ACS applied materials & interfaces
|October 1, 2025
PubMed
概括

机器学习潜能准确地模拟无形GeSe,揭示了对Ovonic Threshold Switching (OTS) 存储器设备至关重要的两个缺陷类型. 这些与特定原子结构相关的缺陷解释了非易失性记忆中的切换行为.

关键词:
没有形态的GeSeSe.密度函数理论密度函数理论电子结构 电子结构机器学习 原子间潜力 原子间潜力中间间隙缺陷的缺陷转换 Ovonic 的值值.

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