Accelerating point-defect simulations using data-driven and machine learning approaches

Point defects in solid-state materials are now routinely simulated using large supercell structures, requiring efficient quantum mechanical solutions. Data-driven and machine learning (ML) models trained on computational data can enable rapid defect property predictions and high-throughput screening. In this article, we provide an overview of prominent efforts to accelerate defect simulations using these approaches. We begin by discussing the motivations for data-driven techniques in defect modeling, and describe efforts over the past decade to use descriptor-based models for rapid screening of defect properties—most notably in oxides. In particular, we discuss case studies where surrogate models and interatomic potentials were trained on density functional theory (DFT) data, leading to predictions with quantum mechanical accuracies at a fraction of the cost. In addition to geometry relaxation and formation energy predictions, these interatomic potentials are capable of predicting phonon modes and vibrational free energies to yield defect energetics at finite temperatures—representing a key frontier for computational defect research. We finish with a discussion on how to connect these approaches and their outputs with experimental data, and provide an outlook on this burgeoning subfield.