Supercell size: Large supercells can describe higher degrees of disorder, allow larger cluster sampling cutoffs, and reduce errors due to the periodic boundary condition, but at the expense of higher computational costs. The upper bound of the cutoff radius (the largest interaction b a Figure | Supercell size and fitting methods benchmarking in terms of accuracy and computational costs. (a) The averaged RMSE (marked in blue squares) and VASP runtime (marked in purple squares) of 9 cubic structures at different supercell sizes . The lines show the trend of RMSE and runtime as supercell size increases. The averaged numbers of atoms in supercells across the 9 cubic structures are 120 (16 Å), 229 (20 Å), and 388 (24 Å), respectively. (b) The averaged RMSE (marked in blue squares), HiPhive runtime (marked in purple squares), and the ratio of converged fittings (% converged, marked in orange squares) across various fitting methods for the same benchmark suite of 9 cubic structures. Here ARDR, EN, LASSO, LS, OMP, RFE, and RIDGE in fitting method represent Automatic Relevance Determination Regression, Elas-ticNet regularization, Least Absolute Shrinkage and Selection Operator, Least Squares, Orthogonal Matching Pursuit, Recursive Feature Elimination and Ridge regression, respectively. distance of any order IFC) is capped by the supercell size. In theory, accuracy should improve as the cutoff radii for IFC fitting and supercell sizes increase because a larger number of IFC components can more physically describe lattice dynamics (barring overfitting). For instance, Eriksson et al. showed that the phonon modes of monolayer MoS 2 are converging when the second-order cutoff is larger than 9 Å, requiring a supercell lattice of at least 18 Å. However, larger supercells incur greater CPU and memory requirements.