Significance of the Chemical Environment of an Element in Nonadiabatic Molecular Dynamics: Feature Selection and Dimensionality Reduction with Machine Learning
Significance of the Chemical Environment of an Element in Nonadiabatic Molecular Dynamics: Feature Selection and Dimensionality Reduction with Machine Learning
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DOI：
10.1021/acs.jpclett.1c03469
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发表时间：
2021-12-09
影响因子：
5.7
通讯作者：
V. Prezhdo, Oleg
中科院分区：
文献类型：
--
作者：
Bin How, Wei;Wang, Bipeng;V. Prezhdo, Oleg
利用有监督和无监督机器学习（ML）对经典路径近似下非绝热（NA）分子动力学（MD）轨迹生成的特征进行分析，证明了具有NA哈密顿量的互信息可用于特征选择和模型简化。以CsPbI3（一种常见的金属卤化物钙钛矿）为例，我们观察到单元素的化学环境足以预测NA哈密顿量。这一结论甚至适用于c，尽管c对相关波函数没有贡献。原子间距离和八面体倾斜角是最重要的特征。我们将典型的360参数ML力场模型简化为仅12参数NA哈密顿模型，同时保持高NA- md模拟质量。由于NA-MD是研究激发态过程的宝贵工具，通过简单的ML模型克服其高昂的计算成本将简化NA-MD仿真，并扩大可访问的系统规模和仿真时间范围。
Using supervised and unsupervised machine learning (ML) on features generated from nonadiabatic (NA) molecular dynamics (MD) trajectories under the classical path approximation, we demonstrate that mutual information with the NA Hamiltonian can be used for feature selection and model simplification. Focusing on CsPbI3, a popular metal halide perovskite, we observe that the chemical environment of a single element is sufficient for predicting the NA Hamiltonian. The conclusion applies even to Cs, although Cs does not contribute to the relevant wave functions. Interatomic distances between Cs and I or Pb and the octahedral tilt angle are the most important features. We reduce a typical 360-parameter ML force-field model to just a 12-parameter NA Hamiltonian model, while maintaining a high NA-MD simulation quality. Because NA-MD is a valuable tool for studying excited state processes, overcoming its high computational cost through simple ML models will streamline NA-MD simulations and expand the ranges of accessible system size and simulation time.