在由适应性采样点云定义的多边形上最温和的上升动力学.
Juan M Bello-Rivas1, Anastasia Georgiou1, Hannes Vandecasteele2
1Department of Chemical and Biomolecular Engineering, Whiting School of Engineering, Johns Hopkins University, 3400 North Charles Street, Baltimore, Maryland 21218, United States.
The journal of physical chemistry. B
|June 6, 2023
概括
最温和的上升动力学 (GAD) 现在使用内在的,数据驱动的方法在复杂的分子系统中找到坐点. 这种方法适应点云多元组,简化了罕见事件研究.
科学领域:
- 计算化学是一种计算化学.
- 动态系统理论 动态系统理论
- 数据驱动的建模.
背景情况:
- 寻找坐点对于理解分子动态中的罕见事件至关重要.
- 现有的Gentlest Ascent Dynamics (GAD) 算法可以找到坐点,但对复杂系统有局限性.
- 之前的GAD概括使用了对具有平等约束的多元体的外部公式.
研究的目的:
- 扩展Gentlest Ascent Dynamics (GAD) 用于在由点云定义的多元体上找到点.
- 开发一个纯粹数据驱动的,GAD的内在配方.
- 为了使在没有明确的约束方程的情况下研究分子系统中的罕见事件.
主要方法:
- 为点云定义的多元体开发了GAD的内在配方.
- 在代过程中采用点云的自适应采样.
- 使用数据驱动的方法,只需要初始构成 (反应物).
主要成果:
- 成功地扩展了GAD,以处理使用内在视角的点云表示的分组.
- 该方法适应性地采样数据点,以导航到位点.
- 该方法纯粹是数据驱动的,不需要明确的约束方程.
结论:
- 新的内在GAD扩展有效地在点云分流器上找到坐点.
- 这种数据驱动的方法简化了分子系统中罕见事件的研究.
- 该方法为计算化学和动态系统分析提供了强大的工具.
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