论文标题
与时间相关的Kohn-Sham系统中动态的机器学习Kohn-Sham潜力
Machine-learning Kohn-Sham potential from dynamics in time-dependent Kohn-Sham systems
论文作者
论文摘要
在时间依赖性密度功能理论（TDDFT）中构建更好的交换相关潜力可以提高TDDFT计算的准确性，并为多电子系统的性质提供更准确的预测。在这里，我们提出了一种开发能量功能的机器学习方法，并提出了时间依赖的Kohn-Sham系统的Kohn-Sham潜力。该方法基于Kohn-Sham系统的动力学，不需要任何有关训练模型的Kohn-Sham潜力的数据。我们以1D谐波振荡器的示例和1D两电子示例来证明我们的方法的结果。我们表明，在没有记忆效果的情况下，机器学习的Kohn-Sham电位与确切的Kohn-Sham潜力相匹配。我们的方法仍然可以在存在内存效应的情况下捕获Kohn-Sham系统的动力学。本文开发的机器学习方法提供了有关在时间依赖性Kohn-Sham系统中更好地近似能量功能和Kohn-Sham潜力的洞察力。
The construction of a better exchange-correlation potential in time-dependent density functional theory (TDDFT) can improve the accuracy of TDDFT calculations and provide more accurate predictions of the properties of many-electron systems. Here, we propose a machine learning method to develop the energy functional and the Kohn-Sham potential of a time-dependent Kohn-Sham system is proposed. The method is based on the dynamics of the Kohn-Sham system and does not require any data on the exact Kohn-Sham potential for training the model. We demonstrate the results of our method with a 1D harmonic oscillator example and a 1D two-electron example. We show that the machine-learned Kohn-Sham potential matches the exact Kohn-Sham potential in the absence of memory effect. Our method can still capture the dynamics of the Kohn-Sham system in the presence of memory effects. The machine learning method developed in this article provides insight into making better approximations of the energy functional and the Kohn-Sham potential in the time-dependent Kohn-Sham system.
