Deep learning has significantly changed the fields of speech recognition, computer vision and natural language processing, to name a few. What’s next? We are currently witnessing fast progress in the application of deep learning to scientific computation, such as protein folding, weather prediction, and molecular simulation. In this talk I will discuss a number of important tools and concepts that are pushing the frontier of this new disruption. In particular I will discuss graph neural networks for the prediction of molecular properties and the numerical integration of PDEs, and flows for molecular synthesis. We also discuss the important concept of incorporating symmetries in these models through equivariant layers. We will end with some thoughts on the impact on sustainability and health that these new breakthroughs might enable.