半古典的多态动力学为六个相结合的O+O2状态
Farideh Badichi Akher1, Yinan Shu1, Zoltan Varga1
1Department of Chemistry, Chemical Theory Center and Minnesota Supercomputing Institute, University of Minnesota, Minneapolis, Minnesota 55455-0431, United States.
Journal of chemical theory and computation
|July 13, 2023
概括
新的深度神经网络方法准确地模拟高能O2-O碰撞,用于超音速车辆流. 这通过有效计算潜在能量表面和状态合,提高了对热能和热流的理解.
科学领域:
- 化学物理 化学物理
- 计算化学的计算化学
- 航空航天工程 航空航天工程
背景情况:
- 精确模拟高能O2-O碰撞对于建模超音速车辆空气动力学,特别是热能含量和热流量至关重要.
- 高效的动力学模拟需要精确的全球潜在能量表面和许多电子状态的状态合,这是一个长期存在的计算挑战.
- 深度神经网络的最新进展为解决复杂的多状态碰撞动态提供了新的方法.
研究的目的:
- 将深度神经网络 (PM-DDNN) 方法的新型参数管理型糖化应用于O3系统,用于同时糖化和潜在能量表面的装配.
- 用三种不同的方法:CSDM, κCSDM 和 eκCSDM 来执行和比较使用从 PM-DDNN 衍生出来的亚底面的动态计算.
- 通过各种碰撞能量,振动和旋转等级来研究电子非相应,不弹性和离散的横截面的行为.
主要方法:
- 利用PM-DDNN方法,一个具有数据依赖激活功能的深度神经网络,以确定糖尿病潜在能量矩阵 (DPEM).
- 通过对角化装配的DPEM来生成附加的潜在能量表面,使用扩展的多态完整活性空间二次扰动理论的数据.
- 使用CSDM, κCSDM和eκCSDM进行了分子动力学模拟, κCSDM只需要附带能量和梯度.
主要成果:
- PM-DDNN方法成功地应用于O3.5的5A'分组中的6个能量最低的潜在能量表面.
- 使用CSDM, κCSDM和eκCSDM进行的动力学计算显示出良好的一致性,验证了生成的亚底面的准确性.
- 针对各种碰撞条件计算的截面显示出由潜在能量表面之间的能量差距合理化的趋势.
结论:
- PM-DDNN方法提供了一种高效且准确的方法,用于获得多态碰撞动态模拟所需的潜在能量表面.
- 这项研究表明,先进的神经网络技术能够解决计算化学和物理学的长期挑战.
- 了解潜在能量表面间隙和不弹性横截面之间的关系是预测高超音速流动行为的关键.
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