Molecular dynamics simulations are very powerful, providing microscopic insight into chemical and physical processes. However, two outstanding challenges of the standard algorithms are: 1) Extending simulations to longer timescales, to allow the description of phenomena such as the nucleation and growth of crystals. 2) Including quantum statistics at a reasonable computational cost, to enable studying quantum materials using classical algorithms. We harness the power of machine learning (and other) algorithms for overcoming these limitations.