Phase transitions of zirconia: Machine-learned force fields beyond density functional theory

Machine-learned force fields (MLFFs) are becoming an increasingly important tool in materials science and physics. However, most MLFFs constructed based on density functional theory (DFT) calculations, which come with significant limitations. Here, the authors combine efficient on-the-fly active learning procedure a ∆-machine approach, enabling generation of accuracy that exceeds DFT at modest computational cost. Using this method, they generated MLFF for random phase approximation allows highly accurate predictions transition temperatures zirconia.