Direct Learning Hidden Excited State Interaction Patterns from ab initio Dynamics and Its Implication as Alternative Molecular Mechanism Models.
Direct Learning Hidden Excited State Interaction Patterns from ab initio Dynamics and Its Implication as Alternative Molecular Mechanism Models.
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从头算动力学直接学习隐藏激发态相互作用模式及其作为替代分子机制模型的含义
DOI：
10.1038/s41598-017-09347-2
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发表时间：
2017-08-18
影响因子：
4.6
通讯作者：
Gao J
中科院分区：
文献类型：
--
作者：
Liu F;Du L;Zhang D;Gao J
多原子系统的激发态相当复杂，往往呈现亚稳态动力学行为。由于反应路径的高度非平衡性，对反应路径的静态分析往往不能充分描述激发态运动。在这里，我们提出了一种时间序列引导的聚类算法，直接从从头算动态轨迹中生成最相关的亚稳定模式。基于对这些亚稳态模式的了解，我们提出了一种只用一组具体的、有限的已知模式来准确预测整个动力学轨迹的基态和激发态性质的内插方案，即系综模型预测(PEM)。以芥子酸为例，除了聚类算法外，PEM方法不需要任何训练数据，并且基态和激发态的估计误差非常接近，这表明可以用类似的精度来预测基态和激发态的分子性质。这些结果可能会为我们构建与传统力场一样具有相容能量项的分子机制模型提供一些启示。
The excited states of polyatomic systems are rather complex, and often exhibit meta-stable dynamical behaviors. Static analysis of reaction pathway often fails to sufficiently characterize excited state motions due to their highly non-equilibrium nature. Here, we proposed a time series guided clustering algorithm to generate most relevant meta-stable patterns directly from ab initio dynamic trajectories. Based on the knowledge of these meta-stable patterns, we suggested an interpolation scheme with only a concrete and finite set of known patterns to accurately predict the ground and excited state properties of the entire dynamics trajectories, namely, the prediction with ensemble models (PEM). As illustrated with the example of sinapic acids, The PEM method does not require any training data beyond the clustering algorithm, and the estimation error for both ground and excited state is very close, which indicates one could predict the ground and excited state molecular properties with similar accuracy. These results may provide us some insights to construct molecular mechanism models with compatible energy terms as traditional force fields.
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影响因子：
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作者：
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通讯作者：
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影响因子：
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