  Molecular dynamics simulations provide a mechanistic description of molecules
by relying on empirical potentials. The quality and transferability of such
potentials can be improved leveraging data-driven models derived with machine
learning approaches. Here, we present TorchMD, a framework for molecular
simulations with mixed classical and machine learning potentials. All of force
computations including bond, angle, dihedral, Lennard-Jones and Coulomb
interactions are expressed as PyTorch arrays and operations. Moreover, TorchMD
enables learning and simulating neural network potentials. We validate it using
standard Amber all-atom simulations, learning an ab-initio potential,
performing an end-to-end training and finally learning and simulating a
coarse-grained model for protein folding. We believe that TorchMD provides a
useful tool-set to support molecular simulations of machine learning
potentials. Code and data are freely available at \url{github.com/torchmd}.
