Molecular dynamics is an exceptional simulation tool that connects fundamental physics with more applied research. It is the workhorse in our collaborations with experimental researchers to enrich their findings with computational predictions and insights. Such simulations rely on an interatomic force model, which is always a trade-off between computational efficiency, accuracy, and the ability to describe all relevant physics. Recently, we proposed the "electron Machine Learning Potential" (eMLP), which reconciles these requirements in an unparalleled way: it is not only fast and accurate, but also able to describe physical phenomena inaccessible to analoguous models, such as spectra (direct comparison with experiment), and charge transport (important for energy materials). Our goal is to elevate the eMLP from a promising prototype to a robust simulation tool, and to showcase its strengths with unrivaled simulations of solid electrolyte conductivity and Raman spectra.