Sometimes the computational techniques at our disposal are not sufficient. This typically happens because a model needs to capture phenomena across many spatiotemporal scales, or study material compositions in a large design space. When needed, we expand our toolbox! Particularly, we focus on coarse-grained molecular dynamics and we use machine learning algorithms to develop new force-fields and efficiently sample combinations of molecular building blocks.