This study pioneers the application of the latest generation of machine learning techniques to directly predict dynamical ssNMR observables at microsecond timescales from atomistic simulations. It is important to stress that an ssNMR calculation with DFT accuracy on the µs timescale would not be possible without leveraging machine learning to predict the EFG tensors. Calculating EFG tensors for the 576 atom am-LPS unit cell over a 1 µs simulation would cost roughly 22.5 million CPUh, with snapshots taken every 100 ps. The same prediction made using the ML-EFG model uses 500 CPUh. This is a factor of 45,000 speedup over DFT-calculated EFG tensors. Therefore, this is, to the best of our knowledge, the first dynamical ssNMR calculation performed at DFT level accuracy, and on an experimentally relevant timescale.