Machine learning for molecular and materials science

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 atoms and hundreds of GPUS, and to the calculations of other properties such as dipoles, NMR chemical shifts, IR spectra, etc.