In order to establish the relationship between fragment geometry and tensorial asymptotic parameters, we adopt the architecture of a SOTA E3NN called Neural Equivariant Interatomic Potentials (NequIP). NequIP is the first equivariant tensor graph convolutional neural network. During the message passing process, previous neural networks such as SchNet were limited to aggregating scalar information, thus restricting their application to the prediction of scalar properties like molecule energy. In contrast, NequIP aggregates both atom i is first encoded using a trainable embedding network, represented as x 0 i . Subsequently, the environment is encoded using the relative position from atom i to it's neighboring atom j: