结合线性缩放量子运输和机器学习分子动力学,研究复杂材料中的热和电子运输
Zheyong Fan1, Yang Xiao1, Yanzhou Wang2
1College of Physical Science and Technology, Bohai University, Jinzhou 121013, People's Republic of China.
概括
我们开发了一种统一的方法来利用机器学习潜力和分子动力学预测材料中的热和电子传输特性. 这种方法准确地模拟复杂的材料行为,以提高性能.
科学领域:
- 材料科学 材料科学 材料科学
- 计算物理 计算物理
- 凝聚物质物理学 凝聚物质物理学
背景情况:
- 预测材料特性对于设计先进材料至关重要.
- 传统的方法经常与复杂的系统和计算成本作斗争.
- 精确模拟热和电子传输是一个挑战.
研究的目的:
- 提出一种高效,统一的方法,同时预测热和电子传输特性.
- 开发一种机器学习的神经进化潜力 (NEP),用于精确的材料模拟.
- 为了证明这种方法在研究基于石墨烯的系统中的实用性.
主要方法:
- 用密度函数理论数据训练机器学习的神经进化潜力 (NEP).
- 采用大型分子动力学模拟用于结构生成和热传输.
- 将分子动力学与对电子性质的线性缩放量子传输计算结合起来.
- 调查电子声波散射和其他障碍效应.
主要成果:
- 成功培训了碳系统的高效NEP.
- 生成现实的结构和准确的特征热传输特性.
- 在原始石墨烯中证明了电子运输的准确预测.
- 分析了石墨烯抗毒格子的热电特性.
结论:
- 统一的方法可以有效和准确地预测合运输属性.
- 与分子动力学和量子运输相结合的NEP是材料发现的强大工具.
- 该方法促进了对电子和热电应用材料的理解和设计.
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