Department of Chemistry, Stanford University, Stanford, California 94305, United States.
J Phys Chem A. 2020 Jul 16;124(28):5979-5992. doi: 10.1021/acs.jpca.0c04313. Epub 2020 Jul 3.
The center line slope (CLS) observable has become a popular method for characterizing spectral diffusion dynamics in two-dimensional (2D) correlation spectroscopy because of its ease of implementation, robustness, and clear theoretical relationship to the frequency-frequency correlation function (FFCF). The FFCF relates the frequency fluctuations of an ensemble of chromophores to coupled bath modes of the chemical system and is used for comparison to molecular dynamics simulations and for calculating 2D spectra. While in the appropriate limits, the CLS can be shown to be the normalized FFCF, from which the full FFCF can be obtained, in practice the assumptions that relate the CLS to the normalized FFCF are frequently violated. These violations are due to the presence of homogeneous broadening and motional narrowing. The generalized problem of relating the CLS to the FFCF is reanalyzed by introducing a new set of dimensionless parameters for both the CLS and FFCF. A large data set was generated of CLS parameters derived from numerically modeled 2D line shapes with known FFCF parameters. This data set was used to train feedforward artificial neural networks that act as functions, which take the CLS parameters as inputs and return FFCF parameters. These neural networks were deployed in an algorithm that is able to quickly and accurately determine FFCF parameters from experimental CLS parameters and the fwhm of the absorption line shape. The method and necessary inputs to accurately obtain the FFCF from the CLS are presented.
中线斜率 (CLS) 可观测量已成为二维 (2D) 相关光谱学中表征光谱扩散动力学的一种流行方法，因为它易于实现、稳健且与频率-频率相关函数 (FFCF) 具有明确的理论关系。FFCF 将一组发色团的频率波动与化学系统的耦合浴模式相关联，并用于与分子动力学模拟进行比较以及计算 2D 光谱。虽然在适当的限制下，可以证明 CLS 是归一化的 FFCF，从中可以获得完整的 FFCF，但实际上，将 CLS 与归一化 FFCF 相关联的假设经常被违反。这些违反是由于存在均匀展宽和运动变窄。通过引入 CLS 和 FFCF 的新一组无量纲参数，重新分析了将 CLS 与 FFCF 相关联的广义问题。使用具有已知 FFCF 参数的数值模拟 2D 线形状生成了一个 CLS 参数的大型数据集。该数据集用于训练前馈人工神经网络，这些神经网络作为函数，将 CLS 参数作为输入并返回 FFCF 参数。这些神经网络被部署在一种算法中，该算法能够快速准确地从实验 CLS 参数和吸收线形状的半峰全宽确定 FFCF 参数。介绍了从 CLS 准确获得 FFCF 的方法和必要输入。
