基于机器学习的大规模非亚亚巴特动力学模拟哈密尔顿和力场:单层MoS中电荷传输的情况2
Bichuan Cao1, Jiawei Dong1, Zedong Wang1
1Zhejiang Key Laboratory of Excited-State Energy Conversion and Energy Storage, Department of Chemistry, Zhejiang University, Hangzhou 310058, China.
我们开发了一种机器学习方法,用于高效的大规模非adiabatic动力学模拟. 这种方法显著降低了计算成本,使得像MoS2.2这样的材料可以进行准确的电子传输研究.
科学领域:
- 计算物理 计算物理
- 材料科学 材料科学 材料科学
- 量子化学 是一个量子化学.
背景情况:
- 精确模拟电荷载体动态对于理解材料特性至关重要.
- 像密度函数理论 (DFT) 这样的传统方法对于大型系统来说在计算上是昂贵的.
研究的目的:
- 开发一种高效可靠的大规模非adiabatic动力学模拟方法.
- 为了能够准确地模拟材料中的电子传输.
主要方法:
- 开发了在结构描述器上训练的准对话式哈密尔顿网络 (DHNet).
- 使用Wannier的基础来捕获本地和非本地环境信息.
- 结合DHNet与DeePMD机器学习力场和表面跳跃方法.
主要成果:
- 与DFT相比,DHNet显著降低了大约5个数量级的计算成本.
- 成功模拟了超过3000个原子的单层MoS2中的电子传输.
- 对于MoS2,计算的电子流动性为110cm^2/(Vs,与实验值相匹配.
结论:
- DHNet方法为构建电子哈密尔顿数提供了一种高效和可转移的方法.
- 对于复杂的材料系统,大规模的非adiabatic动力学模拟是可行的.
- 这种方法在研究各种材料的电荷载体动力学方面具有广泛的潜力.
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