We introduce JAX MD, a software package for performing differentiable physics simulations with a focus on molecular dynamics. JAX MD includes a number of physics simulation environments, as well as interaction potentials and neural networks that can be integrated into these environments without writing any additional code. Since the simulations themselves are differentiable functions, entire trajectories can be differentiated to perform meta-optimization. These features are built on primitive operations, such as spatial partitioning, that allow simulations to scale to hundreds-of-thousands of particles on a single GPU. These primitives are flexible enough that they can be used to scale up workloads outside of molecular dynamics. We present several examples that highlight the features of JAX MD including: integration of graph neural networks into traditional simulations, meta-optimization through minimization of particle packings, and a multi-agent flocking simulation. JAX MD is available at www.github.com/google/jax-md.
Efficient GPU-computing simulation platform JAX-PF for differentiable phase field model
Northwestern University（西北大学）；Theoretical and Applied Mechanics, Northwestern University（理论与应用力学系，西北大学）；HIDENN-AI, LLC（HIDENN-AI有限责任公司）；Department of Materials Science and Engineering, The Ohio State University（材料科学与工程系，俄亥俄州立大学）
