在Fe-Pt合金中原子排序的驱动力,通过密度函数理论和机器学习的原子间潜力蒙特卡洛模拟进行研究
Tomoyuki Tsuyama1, Takeshi Kaneshita1, Akira Matsui1
1Resonac Corporation, Research Center for Computational Science and Informatics 8, Ebisu-cho, Kanagawa-ku, Yokohama, Kanagawa 221-8517, Japan.
旋转极化对于理解Fe-Pt合金中的原子排序至关重要,它显著影响形成和过渡温度. 在模拟中包括旋转效应,可以准确地预测合金的行为,即使在克里温度以上.
科学领域:
- 材料科学 材料科学 材料科学
- 计算材料科学科学 计算材料科学
- 凝聚物质物理学 凝聚物质物理学
背景情况:
- 像Fe-Pt这样的双金属合金中的原子排序会影响它们的性能.
- 准确预测相位转换需要了解潜在的能量贡献.
研究的目的:
- 阐明Fe-Pt双金属合金中原子排序的机制.
- 调查自旋偏振在形成和相变中的作用.
主要方法:
- 密度函数理论 (DFT) 计算以确定形成度.
- 机器学习的原子间潜力蒙特卡洛 (MLIP-MC) 模拟用于有限温度行为.
- 分析状态的电子密度,以了解能量收益.
主要成果:
- 旋转极化显著增强了有序Fe-Pt相的形成度.
- 考虑到旋转,局部潜力的一致性在有序阶段提供了能量增益.
- 使用旋转偏振的MLIP-MC模拟显示与实验和热力学数据的良好一致.
结论:
- 旋转极化对于精确建模Fe-Pt合金中的原子排序至关重要.
- 包含旋转效应对于预测过渡温度至关重要,即使在基里温度以上.
- 短距离旋转顺序可能会持续在基里温度以上,影响合金的行为.
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