Frequency (meV) The training set can be augmented over multiple fitting trials until a desired accuracy is met. For example, Atom-ate2 facilitates scaling the number of additional training structures and the order of anharmonic fitting based on harmonic fitting outcomes, allowing for dynamic and iterative addition of training structures until a desired numerical accuracy is met. Another significant advantage of Atomate2 is its flexibility in swapping the DFT calculator for a surrogate model, such as a machine-learning (ML) model. This allows for running workflows with ML frameworks like MACE , M3GNet , CHGNet , or other machine-learning force fields (MLFFs) supported by Atomate2 without any modifications to the workflow.