Two-dimensional electronic spectroscopy has become one of the main experimental tools for analyzing the dynamics of excitonic energy transfer in large molecular complexes. Simplified theoretical models are usually employed to extract model parameters from the experimental spectral data. Here we show that computationally expensive but exact theoretical methods encoded into a neural network can be used to extract model parameters and infer structural information such as dipole orientation from two dimensional electronic spectra (2DES) or reversely, to produce 2DES from model parameters. We propose to use machine learning as a tool to predict unknown parameters in the models underlying recorded spectra and as a way to encode computationally expensive numerical methods into efficient prediction tools. We showcase the use of a trained neural network to efficiently compute disordered averaged spectra and demonstrate that disorder averaging has non-trivial effects for polarization controlled 2DES.

二维电子光谱已成为分析大分子复合物中激子能量转移动力学的主要实验工具之一。通常采用简化的理论模型从实验光谱数据中提取模型参数。在这里，我们证明了可以将编码到神经网络中的计算量大但精确的理论方法用于提取模型参数，并从二维电子光谱（2DES）或相反地推断结构信息（如偶极子方向），从而从模型参数中生成2DES。我们建议使用机器学习作为一种工具来预测所记录光谱的模型中的未知参数，并将其作为将计算上昂贵的数值方法编码为有效的预测工具的一种方式。