Abstract

Chromophores and their photodynamics play a fundamental role in controlling biological functions, ranging from photosynthesis to visual perception, and in converting solar energy into the chemical energy stored in liquid solar fuels. These essential processes are finely tuned by the interactions between a chromophore and its complex environment. Time-resolved, multidimensional optical spectroscopies provide a key tool to investigate these processes. However, linking these spectroscopies to the electronic and nuclear dynamics that give rise to them in the condensed phase remains a theoretical challenge due to the need to accurately describe the ground and excited state electronic surfaces and combine them with methods that can capture spectral signatures arising from dynamical effects, such as vibronic progressions, as well as nuclear quantum effects. In this talk I will introduce the different levels of treatment that can be applied to capture the linear and multidimensional optical spectroscopy of condensed phase chromophores and demonstrate how the most accurate dynamics-based approaches can be made practical by constructing machine learning models that greatly accelerate the calculation of multidimensional optical spectra from first principles.