DescriptionMachine learning (ML) methods have dramatically changed molecular simulations for material and biophysical applications. They can provide highly accurate models without increasing the computational effort, i.e., a bridge between classical quantum, atomistic, and coarse-grained spatiotemporal scales. However, several pressing issues still need to be addressed. Namely, the transferability of ML models (to unseen configurations, molecules, and thermodynamic states) and the stability of ML-driven simulations (avoiding unphysical states, e.g., overlapping particles). To this end, several novel approaches were recently proposed ranging from the sophisticated construction of training databases to the incorporation of physics knowledge. This minisymposium aims to present state-of-the-art ML methods for molecular modeling and simulations. Additionally, it will provide a platform to discuss the current challenges and share knowledge and ideas across different applications and modeling scales. While the primary focus of the minisymposium is on material science and biophysics applications, the novel methodologies tackling data scarcity and prediction uncertainty are transferable to continuum modeling and applications.