发表机构
Rutgers University（罗格斯大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究针对高温原子缺陷识别的无标签问题，提出多级域对齐框架，结合等变去噪、对比学习与形态正则化，实现无高温标签下的高精度缺陷识别，可应用于百万原子级分子动力学模拟。
AI 中文摘要
高温下原子缺陷的识别十分困难，因为热波动会模糊几何启发式方法和监督分类器所依赖的局部对称性：低温参考构型中存在可信标签，而对鲁棒分析至关重要的高温区域实际上是无标签的。我们将此问题视为跨温度域偏移问题，并在三个级别对齐两个域：输入级别使用等变去噪器，表示级别使用跨温度对比学习，形态感知正则化器则引导预测结果趋近于物理缺陷结构的紧凑几何形态。由于高温下不存在原子级真值，我们进一步引入了无标签评估套件，该套件沿五个基于空间和物理的轴对预测的缺陷结构进行评分，无需高温标签即可实现模型评估与选择。在接近熔点时，该框架在面心立方、体心立方和密排六方铁体系中识别空位和自间隙原子，针对Wigner-Seitz基准真值，所有间隙原子均被定位，所有空位系统均无假阳性检测，且训练过程中未使用任何高温标签。该框架在包含百万个原子、时长2.5纳秒的轨迹上保持此保真度，可分辨单空位跳跃和完整的弗伦克尔对复合，并捕捉铝双晶中的晶界相变，区分两种形核模式。因此，多级域对齐为大规模分子动力学的温度鲁棒结构分析提供了一种实用、标签高效的途径。
英文摘要
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-wise truth exists at temperature, we further introduce a label-free evaluation suite that scores predicted defect structures along five spatial and physics-based axes, enabling model assessment and selection without high-temperature labels. Near the melting point, the framework identifies vacancies and self-interstitial atoms across face-centered-cubic, body-centered-cubic, and hexagonal-close-packed iron systems with every interstitial localized and zero false detections in every vacancy system against Wigner-Seitz ground truth, with no high-temperature labels used in training. It sustains this fidelity on a million-atom, 2.5 ns trajectory, resolving single vacancy hops and complete Frenkel-pair recombination, and captures grain-boundary phase transformations in aluminum bicrystals, distinguishing two nucleation modes. Multi-level domain alignment thus offers a practical, label-efficient route to temperature-robust structural analysis of large-scale molecular dynamics.
Comments46 pages, 8 figures
