A literature review of ML techniques for point defect energetics in non-metals that identifies dataset quality as the dominant performance factor and flags charged-defect calculations as the key remaining challenge.
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Machine Learning Approaches to Point Defects in Non-Metallic Materials: A Review of Methods
A literature review of ML techniques for point defect energetics in non-metals that identifies dataset quality as the dominant performance factor and flags charged-defect calculations as the key remaining challenge.