The human genome includes more than 300 genes coding for ion channel proteins, representing approximately 2% of the total number of genes. This abundance of ion channels highlights their critical role in numerous biological processes and their involvement in diseases, underscoring their importance as potential drug targets. Ion channels exert their biological roles through three main functional characteristics: the highly efficient selective conduction of ions; the capacity to open and close in response to chemical/physical stimuli (gating); and the decrease in conductance upon sustained stimuli (inactivation). In the last 20 years, the number of experimental atomic structures of ion channels has increased from a few units to hundreds, now including representative structures for most of the ion channel families. Simulations based on these experimental structures have significantly contributed to the current understanding of conduction, selectivity, gating, and inactivation [1-2]. Strengthening the quantitative agreement between simulations and experiments is now essential for advancing in this field. This effort is currently hampered by common issues in biomolecular simulations, such as the limited timescales for observing biologically relevant events and the sub-optimal accuracy of the underlying physical models. Both of these shortcomings are expected to be mitigated by recent methodological developments. For instance, atomic simulations of ion channels with polarizable force fields have been recently reported [3]. Lack of polarization is a well-known limitation of classical force fields, especially when describing ion-protein and ion-water interactions in a crowded environment like the pore cavity. Consequently, the usage of polarizable force fields is considered a promising strategy for improving the agreement with experimental data about ion conduction and selectivity. An alternative strategy for enhancing the model accuracy in critical channel regions is to combine molecular mechanics with quantum approaches. Thanks to the ever-increasing computational resources, now combined with advancements in codes for hybrid QM/MM models, this approach is becoming feasible for ion channel research [4]. Increasing computational resources, coupled with improved algorithms for accelerating rare events and potentially harnessing machine learning, are also opening new possibilities in the study of state transitions. Gating and inactivation events of ion channels are finally becoming accessible to atomic simulations, offering important insights into the mechanistic functioning of this important protein superfamily [5]. The proposed workshop will foster further developments in the field by bringing together leading scientists in the experimental methodologies and computational techniques used in ion channel research in a stimulating and collaborative environment.