Statistics on Oxygen Vacancy Defects in Amorphous HfO 2 : A Neural‐Network Interatomic Potential Assisted High‐Throughput Prediction

Abstract Accurate statistical prediction of defect properties in amorphous materials is a long‐term challenge, hindering their applications in functional devices. In this work, the oxygen vacancy (Vo) in amorphous hafnium oxide (a‐HfO 2 ) is taken as an example, and we develop a graph‐neural‐network inter‐atomic potential based on the density functional theory (DFT) calculations of 6894 stoichiometric a‐HfO 2 structures and 14219 structures with V O defects, achieving an energy precision of ≈1 meV atom −1 . Combining this potential with the supercell model, the structures and energies of neutral Vo defects can be calculated with DFT‐level accuracy and low computational cost, which enables high‐throughput calculations using supercells with a wide size range, from 96 to 32928 atoms. The results show: i) small supercells with 1000 or fewer atoms cause serious errors in the statistic distribution of Vo formation energies, ii) a converged calculation is possible only when the supercell is up to 1500 atoms, iii) the converged results can also be achieved using the average of various small supercells, e.g., 30 a‐HfO 2 supercells with only 96 atoms. These findings unveil a clear statistics of V O defects in a‐HfO 2 and demonstrate a quantitative accuracy‐estimation criterion for predicting the point defect properties in amorphous materials using supercell models.