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.
Benchmarking and contrasting exchange-correlation functional differences in response to static correlation in unrestricted Kohn-Sham and a hybrid 1-electron reduced density matrix functional theory
Faster Exact Exchange for Solids via occ-RI-K: Application to Combinatorially Optimized Range-Separated Hybrid Functionals for Simple Solids with Pseudopotentials Near the Basis Set Limit
In this work, we developed and showcased the occ-RI-K algorithm to compute the exact exchange contribution in density functional calculations of solids near the basis set limit. Within the gaussian planewave (GPW) density fitting, our algorithm achieves a 1-2 orders of magnitude speedup compared to conventional GPW algorithms. Since our algorithm is well-suited for simulations with large basis sets, we applied it to 12 hybrid density functionals with pseudopotentials and a large uncontracted basis set to assess their performance on band gaps of 25 simple solids near the basis set limit. The la…
Role of exact exchange in thermally-assisted-occupation density functional theory: A proposal of new hybrid schemes
我们提出了将精确交换结合到热辅助占据密度泛函理论（TAO-DFT）中的混合方案[J. - D. Chai，J.Chem.Phys.136，154104（2012）]，以改进对非局部交换效应的描述。通过一些简单的修改，Kohn-Sham密度泛函理论（KS-DFT）中的整体和距离分离的杂化泛函可以与TAO-DFT无缝结合。与KS-DFT中的全局混合泛函相比，TAO-DFT中所得的全局混合泛函对于具有强静态相关效应的系统（例如，H2和N2的解离、扭曲乙烯和线性并苯的电子性质），同时对于没有强静态相关效应的系统保持类似的性能。此外，通过在混合TAO-DFT中引入色散校正，可以有效地实现对非共价相互作用的合理准确描述。相对于TAO-DFT中的半局域密度泛函，TAO-DFT中的全局杂化泛函在热化学、动力学、反应能和优化几何结构等广泛的应用中表现出优越的性能.
Benchmarking exchange-correlation potentials with the mstar60 dataset: Importance of the nonlocal exchange potential for effective mass calculations in semiconductors
First-principles calculations of magnetic states in pyrochlores using a source-corrected exchange and correlation functional
我们提出了第一性原理研究的自旋冰态在Dy $_2$ Ti $_2$ O $_7$使用磁源自由交换和相关功能，实施的Castep电子结构代码。通过比较从传统的本地自旋密度近似的结果，我们表明，自旋冰态Dy $_2$ Ti $_2$ O $_7$可以可靠地通过去除磁源的交换和相关贡献的潜力，我们对比这对其他挫烧绿石磁铁的计算基态。
Uncertainty of DFT calculated mechanical and structural properties of solids due to incompatibility of pseudopotentials and exchange-correlation functionals
