Streamlining analysis and design of two-dimensional electronic spectroscopy using machine learning

Two-dimensional electronic spectroscopy (2DES) offers unique insights into the coupling between electronic and nuclear motion and dynamics, making it a key technique in diverse fields, including materials science and biology. Obtaining 2DES data requires a series of measurements that involve multiple pulses to construct the full picture-a time-consuming task that often necessitates working with limited or noisy data. Here, we introduce a machine-learning based framework that aims to maximize the data that can be extracted from 2DES experiments and provides guidance toward the selection of additional experiments. We design a Gaussian mixture model to learn the underlying spectral density of a system, allowing the extraction of reorganization energies and the extrapolation of the 2DES spectra to other time delays beyond those measured, and demonstrate how our framework can be used to select additional measurements to further improve the accuracy. We show that our approach yields accurate results on a variety of systems, including simulations ranging from photoactive yellow protein in the gas phase to Nile Red in benzene to the anionic green fluorescent protein chromophore in water, and experiments on Nile Blue in ethanol. Our work provides an efficient route to extract maximum insights from 2DES while incurring minimal experimental costs.