SchNetPack is a Python toolbox for developing deep neural networks that predict quantum-chemical properties such as potential energy surfaces for molecules and materials. It provides built-in architectures including SchNet (continuous-filter CNN) and PaiNN (equivariant message-passing), along with modules for electrostatics and molecular dynamics simulation. The framework uses PyTorch and PyTorch Lightning for training and supports GPU-accelerated computations.