I utilized an Equivariant Neural Network (E3NN) within the PyTorch framework to ensure permutation and rototranslational invariance . For a function f exhibiting equivariance to a group G, every element g ∈ G and input x must satisfy f (g • x) = g • f (x). The E3NN model produced output irreducible representations for each atom, yielding a 3-dimensional vector that maintained equivariance as a scalar under rotations. By integrating spherical harmonics into the edge attributes, the model effectively captured the orientation-dependent interactions between atoms. The E3NN model was trained for 30 epochs.