Machine learning (ML) encompasses a wide range of algorithms and models, which have been prominently applied to condensed matter. Some applications range from atomistic simulations, generative quantum and classical distributions, predictors of physicochemical properties, differential equations’ ansatz, among many others. In this talk, we will present some examples of how ML models have advanced our understanding of molecular systems and their complex interactions. In particular, we will focus on how combining machine learned force fields and quantum interatomic dilation, not only reveals the intricate nature of molecular systems, but also shows the limitations of many electronic structure methods. Additionally, we will briefly show some of the current applications of ML to quantum systems in our group, this with particular emphasis to describe excited states in second quantized representation and their paramount importance while describing experimental results.