With MPNNs, a molecular system is usually considered as a 3D graph with atoms as vertices and connections as edges. Atomic features are updated iteratively with information from neighboring atoms through message-passing functions. In 2017, Schütt and coworkers pioneered graph neural network (GNN) potentials (DTNN) with a global distance matrix as input . In 2018, the same group developed SchNet , which applies continuous-filter convolutional layers (CFCLs) to describe more accurate atomic environments and shows better scaling to large systems, in comparison with DTNN. In 2019, Unke and Meuwly developed PhysNet by modifying and adding long-range interaction functions based on SchNet.