论文标题
机器学习交换与时间相关密度功能理论的潜力
Machine Learning Exchange-Correlation Potential in Time Dependent Density Functional Theory
论文作者
论文摘要
我们提出了一种基于机器学习的方法，以发展依赖时间密度功能理论（TDDFT）的交换相关潜力。从时间变化的电子密度到时间依赖性的Kohn-Sham方程中相应相关势的神经网络投影是使用一些精确数据集来训练的，用于电子 - 氢散射的模型系统。我们证明，这种神经网络电位可以在散射过程中捕获时间依赖性相关电位中的复杂结构，并提供正确的散射概率，而标准绝热功能无法获得。我们还表明，可以通过这种机器学习技术将非绝热（或记忆）效应纳入电势，从而显着提高动力学的准确性。这里开发的方法提供了一种新的方法来提高TDDFT的交换相关潜力，这使该理论成为研究各种激发状态现象的更强大的工具。
We propose a machine learning based approach to develop the exchange-correlation potential of time dependent density functional theory (TDDFT). The neural network projection from the time-varying electron densities to the corresponding correlation potentials in the time-dependent Kohn-Sham equation is trained using a few exact datasets for a model system of electron-hydrogen scattering. We demonstrate that this neural network potential can capture the complex structures in the time-dependent correlation potential during the scattering process and provide correct scattering probabilities, which are not obtained by the standard adiabatic functionals. We also show that it is possible to incorporate the nonadiabatic (or memory) effect in the potential with this machine learning technique, which significantly improves the accuracy of the dynamics. The method developed here offers a novel way to improve the exchange-correlation potential of TDDFT, which makes the theory a more powerful tool to study various excited state phenomena.
