Alternatives for resolving the challenge of calculating anharmonic IFCs have emerged in the past decade. Sampling IFCs using high-information-density configurations can make the anharmonic simulations more achievable at a reasonable expense. This procedure is crucial for the subsequent analysis of the thermodynamic properties of materials. Available packages include CSLD , ALAM-ODE , TDEP and HiPhive . Mathematically, these aim to extract IFCs by minimizing ∥F -AΦ∥ 2 subject to physically inspired constraints, where A is the sensing matrix containing the elements of the atomic displacements constructed from sampled configurations and F is a vector of forces on all atoms from the configurations. This approach in principle allows for a one-shot fitting of Φ up to any desired order with relatively few training sets. Minimization algorithms used range from ordinary least-squares to sparse-recovery methods such as L 1 regularization. The emergence of efficient methods for anharmonic IFCs and the subsequent development of tools for using anharmonic IFCs to compute macroscopic thermal properties have paved the road for high-throughput computing. Nevertheless, several practical challenges related to automation remain, which we address in this paper.