Like a Coulomb matrix, this feature also considers the pairwise comparison of the selected properties between two atoms in the lattice. One challenge is that when the number of atoms considered is different, the size of the resultant matrix will also be different. In our specific case, the size of the matrix for 4-, 5-and 6-coordinated Al sites will be different. This is troublesome for machine learning prediction because most algorithms require the dimensionality of the feature space to be uniform across all the samples. To solve the problem, we decompose the matrix with singular value decomposition (SVD) and use 5 singular values, the maximum number of possible singular values for our system, as our features instead of the whole matrix.