Machine-learning Kohn–Sham potential from dynamics in time-dependent Kohn–Sham systems
Machine-learning Kohn–Sham potential from dynamics in time-dependent Kohn–Sham systems
复制标题
机器学习 KohnâSham 时间依赖性 KohnâSham 系统动态中的潜力
DOI：
10.1088/2632-2153/ace8f0
复制
发表时间：
2023
期刊：
影响因子：
--
通讯作者：
Whitfield, James
中科院分区：
文献类型：
--
作者：
Yang, Jun;Whitfield, James
在时间相关密度泛函理论（TDDFT）中构建更好的交换相关势可以提高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，我们提出了一种机器学习方法来开发时间相关 Kohn-Sham (TDKS) 系统的能量泛函和 Kohn-Sham 势。该方法基于 Kohn-Sham 系统的动力学，不需要任何有关确切 Kohn-Sham 潜力的数据来训练模型。我们通过一维谐振子示例和一维二电子示例演示了我们的方法的结果。我们证明，在没有记忆效应的情况下，机器学习的 Kohn-Sham 势与准确的 Kohn-Sham 势相匹配。在存在记忆效应的情况下，我们的方法仍然可以捕获 Kohn-Sham 系统的动态。本文开发的机器学习方法提供了在 TDKS 系统中更好地近似能量泛函和 Kohn-Sham 势的见解。
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 (TDKS) 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 TDKS system.
影响因子：
2.1
作者：
Reddy, Bijivemula N.;Ruddarraju, Radhakrishnam Raju;Reddy, Anreddy Rama Narsimha
通讯作者：
Reddy, Anreddy Rama Narsimha