In the DeePCG framework, the potential energy is decomposed into per-bead contributions, as is common in atomistic ML forcefields. The per-bead energy is predicted by a DNN based on a vector describing the local neighborhood of the bead. This vector is composed of interbead radial and angular terms describing the relationship between the bead and its nearest neighbor, plus radial terms describing the separations between the bead and other neighbors within a cutoff (Section S4). To satisfy translational and rotational invariance, the interbead distances are expressed in terms of a local per-bead coordinate frame determined by the bead and its two nearest neighbors (Section S4).