The excited-state dynamics of chromophores in complex environments determine a range of vital biological and energy capture processes. Time-resolved, multidimensional optical spectroscopies provide a key tool to investigate these processes. Although theory has the potential to decode these spectra in terms of the electronic and atomistic dynamics, the need for large numbers of excited-state electronic structure calculations severely limits first-principles predictions of multidimensional optical spectra for chromophores in the condensed phase. Here, we leverage the locality of chromophore excitations to develop machine learning models to predict the excited-state energy gap of chromophores in complex environments for efficiently constructing linear and multidimensional optical spectra. By analyzing the performance of these models, which span a hierarchy of physical approximations, across a range of chromophore–environment interaction strengths, we provide strategies for the construction of machine learning models that greatly accelerate the calculation of multidimensional optical spectra from first principles.

中文翻译：

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利用机器学习来有效预测复杂环境中的多维光谱。

在复杂环境中，生色团的激发态动力学决定了一系列重要的生物和能量捕获过程。时间分辨的多维光谱学提供了研究这些过程的关键工具。尽管理论上有潜力根据电子和原子动力学对这些光谱进行解码，但是对大量激发态电子结构计算的需求严重限制了凝聚相中生色团的多维光谱的第一性原理预测。在这里，我们利用发色团激发的局部性来开发机器学习模型，以预测复杂环境中发色团的激发态能隙，从而有效地构建线性和多维光谱。通过分析这些模型的性能，