This account is written for general users of time-dependent density functional theory (TD-DFT) methods as well as chemists who are unfamiliar with the field. It includes a brief overview of conventional TD-DFT approaches and recommendations for applications to organic molecules based on our own experience. The main emphasis of this work, however, lies in providing the first in-depth review of time-dependent double-hybrid density functionals. They were first established in 2007 with very promising follow-up studies in the subsequent four years before developments or applications became scarce. The topic has regained more interest since 2017, and this account reviews those latest developments led by our group. These developments have shown unprecedented robustness for a variety of different types of electronic excitations when compared to more conventional TD-DFT methods. In particular, time-dependent double hybrids do not suffer from artificial ghost states and are able to reproduce exciton-coupled absorption spectra. Our latest methods include range separation and belong to the currently best TD-DFT methods for singlet-singlet excitations in organic molecules. While there is still room for improvement and further development in this space, we hope that this account encourages users to adjust their computational protocols to such new methods to provide more real-life testing and scenarios.

该帐户的读者对象是时变密度泛函理论（TD-DFT）方法的一般用户以及不熟悉该领域的化学家。它包括对常规TD-DFT方法的简要概述，并根据我们的经验为有机分子应用提供了建议。但是，这项工作的主要重点在于，对与时间有关的双杂化密度功能进行首次深度综述。它们最初成立于2007年，在随后的四年中开发或应用程序稀缺之前，都进行了非常有希望的后续研究。自2017年以来，该主题重新引起了人们的兴趣，该帐户回顾了我们小组领导的最新动态。与更传统的TD-DFT方法相比，这些发展已显示出对各种不同类型的电子激励具有空前的鲁棒性。特别地，时间相关的双重杂交体不会遭受人工幻影状态的影响，并且能够重现激子耦合的吸收光谱。我们的最新方法包括范围分离，属于目前用于有机分子中单重态单峰激发的最佳TD-DFT方法。尽管在此领域中仍有改进和进一步发展的空间，但我们希望该帐户鼓励用户将其计算协议调整为此类新方法，以提供更多实际测试和方案。时间相关的双重杂化体不会出现人工幻影状态，并且能够复制激子耦合的吸收光谱。我们的最新方法包括范围分离，属于目前用于有机分子中单重态单峰激发的最佳TD-DFT方法。尽管在此领域中仍有改进和进一步发展的空间，但我们希望该帐户鼓励用户将其计算协议调整为此类新方法，以提供更多实际测试和方案。时间相关的双重杂化体不会出现人工幻影状态，并且能够复制激子耦合的吸收光谱。我们的最新方法包括范围分离，属于目前用于有机分子中单重态单峰激发的最佳TD-DFT方法。尽管在此领域中仍有改进和进一步发展的空间，但我们希望该帐户鼓励用户将其计算协议调整为此类新方法，以提供更多实际测试和方案。