The distance matrix, which records the pairwise distances between the atoms in a molecule within a square matrix, is an example of an invariant representation. Additionally, the Coulomb matrix extends the distance matrix by incorporating both geometric information and electrostatic interactions. The offdiagonal elements represent Coulombic repulsion (as scaled inverse distances) between atoms, while the diagonal elements encode a polynomial fit of atomic energies based on the nuclear charge. Although the distance matrix is primarily used for geometric comparisons, the Coulomb matrix, with its additional electrostatic detail, serves as a powerful input for ML predictions in chemistry.