ML-based nonadiabatic dynamics is still not a routine task due to the intrinsic difficulties of accurately predicting multiple excited states. At the moment of writing, there are no unique protocols to create training sets, and this task requires intense legwork for each new molecular system. Moreover, transferability between systems is still challenging, although recent investigations have demonstrated encouraging proof-of-concept results. Nevertheless, once the ML model is ready, it allows semiquantitatively predicting the nonadiabatic dynamics at much lower computational costs than with a quantum-chemical method. Training and predicting nonadiabatic coupling vectors demand special attention because of their narrowly peaked functional features and arbitrary phases. Alternatively, Newton-X allows computing couplings with TD-BA, which, as discussed in Section 3.6, only depends on the excitation energies and their time derivatives.