An On-the-Fly Deep Neural Network for Simulating Time-Resolved Spectroscopy: Predicting the Ultrafast Ring Opening Dynamics of 1,2-Dithiane

Revolutionary developments in ultrafast light source technology are enabling experimental spectro-scopists to probe the structural dynamics of molecules and materials on the femtosecond timescale.
The capacity to investigate ultrafast processes afforded by these resources accordingly inspires the-oreticians to carry out high-level simulations which facilitate the interpretation of the underlying dynamics from ultrafast experiments. In this Article, we implement a Deep Neural Network (DNN) to convert excited-state molecular dynamics simulations into time-resolved spectroscopic signals. Our DNN is trained on-the-fly from first-principles theoretical data obtained from a set of time-evolving molecular dynamics. The train-test process iterates for each time-step of the dynamics data until the network can predict spectra with sufficient accuracy to replace the computationally intensive quan-tum chemistry calculations required to produce them, at which point it simulates the time-resolved spectra for longer timescales. The potential of this approach is demonstrated by probing dynamics of the the ring opening of 1,2-dithiane using sulphur K-edge X-ray absorption spectroscopy. The benefits of this strategy will be more markedly apparent for simulations of larger systems which will exhibit a more notable computational burden, making this approach applicable to the study of a diverse range of complex chemical dynamics.