Very recently, Yu et al. has introduced a novel machine learning potential approach that extends aspects of many-body expansion and the atom-centered approach. Employing a monomeric energy decomposition a just 1-and 2-body structure descriptors, and neural network architecture, this approach provides and efficient and accurate modeling of the complicated potential energy surface of various molecular systems from gas phase to condensed phase. Here, we provide a brief description of this approach. Interested readers are referred to the original paper for more details.