Machine learning models trained on quantum mechanical data can predict defect properties in solids with high accuracy but at much lower computational cost than traditional methods.
Accelerated screening of functional atomic impurities in halide perovskites using high-throughput computations and machine learning,
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Accelerating point defect simulations using data-driven and machine learning approaches
Machine learning models trained on quantum mechanical data can predict defect properties in solids with high accuracy but at much lower computational cost than traditional methods.