Cross-Temperature Defect Identification in Atomistic Simulations via Multi-Level Domain Alignment
Identifying atomic defects at elevated temperature is difficult because thermal fluctuations blur the local symmetry that both geometric heuristics and supervised classifiers rely on: trustworthy labels exist in low-temperature reference configurations, while the high-temperature regime where robust analysis matters most is effectively unlabeled. We cast this as a cross-temperature domain-shift problem and align the two domains at three levels: an equivariant denoiser at the input level, cross-temperature contrastive learning at the representation level, and a morphology-aware regularizer that steers predictions toward the compact geometry of physical defect structures. Because no atom-wi...
| Authors | Yating Fang, Jungmin Kim, Qian Qian Zhao et al. |
|---|---|
| Primary category | cond-mat.mtrl-sci |
| Published | 22 August 2026 |
| Last updated at source | 22 August 2026 |
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