DeepPolaron ML-MD simulations show rutile electrons form Ti-localized polarons hopping along [001] with 39 meV barrier and 4.4e-2 cm2/Vs mobility, while anatase holes form O-localized polarons hopping to second neighbors with 139 meV barrier and 1.4e-3 cm2/Vs mobility.
33 (65) Freysoldt, C.; Neugebauer, J.; Van De Walle, C
2 Pith papers cite this work. Polarity classification is still indexing.
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cond-mat.mtrl-sci 2years
2026 2verdicts
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background 1polarities
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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.
citing papers explorer
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Polaron Transport in TiO$_{2}$ from Machine Learning Molecular Dynamics
DeepPolaron ML-MD simulations show rutile electrons form Ti-localized polarons hopping along [001] with 39 meV barrier and 4.4e-2 cm2/Vs mobility, while anatase holes form O-localized polarons hopping to second neighbors with 139 meV barrier and 1.4e-3 cm2/Vs mobility.
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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.