MACE provides fast and accurate machine learning force fields for molecular dynamics simulations using higher-order equivariant message passing neural networks. The project includes pretrained foundation models (MACE-MP, MACE-OFF, MACE-Polar) for materials science and organic chemistry applications, supports distributed training, CUDA acceleration with cuEquivariance, and integrates with ASE for simulation workflows. It is implemented in Python with PyTorch.