Here we summarize recent progress in machine learning for the chemical sciences. We
outline machine-learning techniques that are suitable for addressing research questions in
this domain, as well as future directions for the field. We envisage a future in which the
design, synthesis, characterization and application of molecules and materials is
accelerated by artificial intelligence.

We willl present our pastm current and future work on the set of Machine Learning potentials
nicknamed ANI, which are able to compute energies and forces from structure, at a cost
similar to a classical force field, but with accuracies of high level quantum mechanics. This
breaks the old" you can be fast or accurate, but not both" problem in the field of molecular
modeling, and allows us to study a number of problem that seemed intractable until a few
years ago.We will present new extensions such as charged systems, scailng to miilions of …