Abstract: The size of chemical space is vast. This makes application of first principles quantum mechanical and advanced statistical mechanics sampling methods to identify binding motifs, conformational equilibria, and reaction pathways extremely challenging, even when considering better physical models, algorithms, or future exascale computing paradigms. If we could develop new and robust machine learning approaches, ideally grounded in physical principles, we would be able to better tackle many fascinating but quite difficult chemical, biological, and materials systems. At present, the application of machine learning to (bio)chemistry is still in its infancy, and I will describe applications ranging from to potential energy surfaces and property predictions from chemical to biophysical systems to see where machine learning is having impact.