howto:tddft
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howto:tddft [2022/07/19 09:30] – ahehn | howto:tddft [2022/07/19 12:04] – ahehn | ||
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====== How to run a LR-TDDFT calculation for absorption and emission spectroscopy ====== | ====== How to run a LR-TDDFT calculation for absorption and emission spectroscopy ====== | ||
- | This is a short tutorial on how to run linear-response time-dependent density functional theory (LR-TDDFT) computations for absorption and emission spectroscopy. The TDDFT module enables a description of excitation energies and excited-state computations within the Tamm-Dancoff approximation (TDA) featuring GGA and hybrid functionals as well as semi-empirical simplified TDA kernels. The details of the implementation can be found in [[https:// | + | This is a short tutorial on how to run linear-response time-dependent density functional theory (LR-TDDFT) computations for absorption and emission spectroscopy. The TDDFT module enables a description of excitation energies and excited-state computations within the Tamm-Dancoff approximation (TDA) featuring GGA and hybrid functionals as well as semi-empirical simplified TDA kernels. The details of the implementation can be found in [[https:// |
- | Note that the current module is based on an earlier TDDFT implementation [[https:// | + | Note that the current module is based on an earlier TDDFT implementation [[https:// |
- | Please cite these papers if you were to use the TDDFT module for the computation of excitation energies or excited-state gradients. | + | Please cite these papers if you were to use the TDDFT module for the computation of excitation energies |
===== Brief theory recap ===== | ===== Brief theory recap ===== | ||
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\begin{aligned} | \begin{aligned} | ||
\mathbf{A} \mathbf{X}^p &= \Omega^p \mathbf{X}^p \, , \\ | \mathbf{A} \mathbf{X}^p &= \Omega^p \mathbf{X}^p \, , \\ | ||
- | \sum_{\kappa k} [ F_{\mu \kappa \sigma} \delta_{ik} - F_{ik \sigma} S_{\mu \kappa} ] X^p_{\kappa k \sigma} + \sum_{\lambda} K_{\mu \lambda \sigma} [\mathbf{X}^p] C_{\lambda i \sigma} & | + | \sum_{\kappa k} [ F_{\mu \kappa \sigma} \delta_{ik} - F_{ik \sigma} S_{\mu \kappa} ] X^p_{\kappa k \sigma} + \sum_{\lambda} K_{\mu \lambda \sigma} [\mathbf{D}^{{\rm{\tiny{X}}}p}] C_{\lambda i \sigma} & |
\end{aligned} | \end{aligned} | ||
\end{equation} | \end{equation} | ||
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\begin{aligned} | \begin{aligned} | ||
F_{\mu \nu \sigma} [\mathbf{D}] &= h_{\mu \nu} + J_{\mu \nu \sigma} [\mathbf{D}] - a_{\rm{\tiny{EX}}}K^{\rm{\tiny{EX}}}_{\mu \nu \sigma} [\mathbf{D}] + V_{\mu \nu \sigma}^{\rm{\tiny{XC}}} \, , \\ | F_{\mu \nu \sigma} [\mathbf{D}] &= h_{\mu \nu} + J_{\mu \nu \sigma} [\mathbf{D}] - a_{\rm{\tiny{EX}}}K^{\rm{\tiny{EX}}}_{\mu \nu \sigma} [\mathbf{D}] + V_{\mu \nu \sigma}^{\rm{\tiny{XC}}} \, , \\ | ||
- | K_{\mu \nu \sigma} [\mathbf{D}^{\rm{\tiny{X}}}] & | + | K_{\mu \nu \sigma} [\mathbf{D}^{{\rm{\tiny{X}}}p}] & |
\end{aligned} | \end{aligned} | ||
\end{equation} | \end{equation} | ||
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\begin{aligned} | \begin{aligned} | ||
D_{\mu \nu \sigma} &= \sum_k C_{\mu k \sigma} C_{\nu k \sigma}^{\rm{T}} \, , \\ | D_{\mu \nu \sigma} &= \sum_k C_{\mu k \sigma} C_{\nu k \sigma}^{\rm{T}} \, , \\ | ||
- | D_{\mu \nu \sigma}^{\rm{\tiny{X}}} &= \frac{1}{2} \sum_{k} ( X^p_{\mu k \sigma} C_{\nu k \sigma}^{\rm{T}} + C_{\mu k \sigma} (X^p_{\nu k \sigma})^{\rm{T}} ) \, . | + | D_{\mu \nu \sigma}^{{\rm{\tiny{X}}}p} &= \frac{1}{2} \sum_{k} ( X^p_{\mu k \sigma} C_{\nu k \sigma}^{\rm{T}} + C_{\mu k \sigma} (X^p_{\nu k \sigma})^{\rm{T}} ) \, . |
\end{aligned} | \end{aligned} | ||
\end{equation} | \end{equation} | ||
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Within the current implementation, | Within the current implementation, | ||
- | Based on Eq.\ (\ref{tda_equation}), excited-state gradients can be formulated based on a variational Lagrangian for each excited state $p$, | + | Based on the TDA eigenvalue problem, excited-state gradients can be formulated based on a variational Lagrangian for each excited state $p$, |
\begin{equation} | \begin{equation} |
howto/tddft.txt · Last modified: 2024/02/24 10:01 by oschuett