Empowering material defect research with machine learning

Abstract

: Material defects govern the performance limits, degradation pathways, and service lifetimes of functional and structural materials, yet their predictive modeling remains difficult because local electronic reconstruction, long-range interactions, rare kinetic events, and finite-temperature effects are coupled across scales. First-principles calculations provide reliable descriptions of defect cores and charge states but are limited by system size, time scale