Machine learning (ML) force fields (FF) trained on highquality reference data have firmly established their place in modeling increasingly complex and large systems through multiple applications unfeasible just a decade ago. Using an on-the-fly machine learning scheme, Jinnouchi et al. obtained entropy-driven phase transitions for hybrid perovskites systems in a matter of weeks, which would require years using only pure quantum chemistry (QC) methods. Using a symmetry-incorporated kernel-based model, Sauceda et al. successfully performed highly-accurate path integral molecular dynamics simulations to obtain novel insights into the localization of benzene-graphene dynamics induced by nuclear quantum effects. Recent advancements in molecular simulations exploit integrated approaches to improve machine learning force fields by combining scalable training on molecular fragments with specialized neural networks to capture both short-and long-range physicochemical interactions accurately. Moreover, new developments in machine learning algorithms facilitate the automatic identification of molecular building blocks, allowing for the creation of coarse-grained force fields, reproducing long-range interactions, and determining reaction coordinates. One of the essential elements that enabled the successes mentioned above is exploiting symmetries present in the systems under study. Symmetries are crucial for QC calculations and ML models, particularly for complex systems.