To address this issue, some of us have lately established an approach to eliminate the influence of the arbitrary phase during the learning process of the ML model. The core idea of this phase-free training lies in the definition of a loss function (L P ), which internally performs a phase correction. This is realized by computing the loss 2 NS-1 -times, i.e., for all possible phase combinations of all coupled electronic states (N S ) of the prediction and find the one that has the lowest error compared to the target.