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基于机器学习潜力的钻石兴奋剂的原子模拟研究.

Yiheng Yan1, Yaochen Yu1, Junwei Hu1

  • 1State Key Laboratory of Solidification Processing, International Center for Materials Discovery, School of Materials Science and Engineering, Northwestern Polytechnical University, Xi'an, 710072, China. haiyang.niu@nwpu.edu.cn.

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概括
此摘要是机器生成的。

控制钻石中的剂比率是制造高效的p型和n型半导体的关键. 特定的-氧和-硫比率可以形成具有理想电子性质的杂质复合物.

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科学领域:

  • 材料科学 材料科学 材料科学
  • 固态物理 固态物理
  • 计算化学计算化学

背景情况:

  • 钻石表现出卓越的半导体性能,但在实现高效的p型和n型兴奋剂方面面临挑战.
  • 研究了联合兴奋剂策略,以克服钻石单元兴奋剂的局限性.

研究的目的:

  • 系统地调查钻石中的氧 (B-O) 和硫 (B-S) 协同兴奋剂.
  • 了解多邦比如何影响杂质复合物形成和电子性质.
  • 为高性能p型和n型钻石确定最佳的协同兴奋剂策略.

主要方法:

  • 采用混合模拟方法,将分子动力学和蒙特卡洛方法结合起来.
  • 利用机器学习潜力进行精确的原子尺度建模.
  • 分析了由此产生的杂质复合物的形成能量和电离能量.

主要成果:

  • 4:1的B-O比率只形成B4O复合体,其形成能量低 (-1.92 eV) 和电离能低 (0.11 eV),非常适合p型钻石.
  • 1:1的B-S比率产生了BS复合物,其形成能量降低,电离能低 (0.56 eV),适用于n型钻石.
  • 4:1的BS比被确定为p型钻石的好处.

结论:

  • 精确控制剂比率对于量身定制钻石的电子特性至关重要.
  • 与 B-O 和 B-S 进行联合剂,为合成高质量的 p 型和 n 型钻石提供了有前途的途径.
  • 这项研究为可控制合成联合化钻石半导体提供了原子规模的见解.