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.
33 (65) Freysoldt, C.; Neugebauer, J.; Van De Walle, C
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cond-mat.mtrl-sci 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
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.