Post #329

JAX Molecular Dynamics I have already mentioned before exciting works from DeepMind about simulating dynamics of particles: each particle is a node in a graph and nearest neighbors are connected with each other, with the goal to predict acceleration at every time step that is provided to physics engine to predict the next state of the particles. The code and data were recently released here. But there is another Google package JAX MD for performing molecular dynamics in JAX, a numpy-like package with autograd, purely in python (paper and colab notebook). Authors claim to have experiments accelerated in GPU with end-to-end training for hundreds of thousands of particles. The work has been accepted to NeurIPS 2020.