AI 中文总结
本文提出融合格罗莫夫-瓦瑟斯坦（FGW）距离结合贝叶斯优化的工作流，构建异质界面原子模型，可高效筛选界面晶格匹配关系，性能优于随机搜索及预训练MACE能量引导方法。
AI 中文摘要
异质界面是从微电子到能量转换与存储等技术的基础，但其构型复杂性使得无法对界面晶格匹配关系（registries）进行详尽的第一性原理筛选。尽管数据驱动方法可减轻这一负担，但仍受限于稀疏的界面数据集。本文提出一种与能量无关的工作流，将相干界面表示为属性图，利用融合格罗莫夫-瓦瑟斯坦（FGW）距离量化其与母体块体环境的相似性，并将该度量与面内晶格匹配空间上的贝叶斯优化相结合。我们针对KI/NaCl、GaP/GaAs和GaN/Al₂O₃界面（涵盖离子、共价及混合键合 regime）评估该方法，采用MACE和密度泛函理论（DFT）进行层级验证。与单点能量景观的对比显示，FGW距离可捕捉依赖晶格匹配的周期性，而界面在结构极值与能量极值间存在偏差，反映出额外的化学特异性贡献。此外，FGW距离与弛豫能量整体相关。在有限筛选预算下，FGW引导的晶格匹配选择始终优于随机搜索，且在不同界面体系上比预训练MACE能量引导的选择更稳健。该工作流将类块体连续性的定性概念转化为定量预筛选准则，实现高效的晶格匹配探索，并为材料发现工作流提供具有物理意义的候选结构。
英文摘要
Heterogeneous interfaces underpin technologies from microelectronics to energy conversion and storage, but their configurational complexity precludes exhaustive first-principles screening of interface registries. Although data-driven approaches can alleviate this burden, they remain limited by sparse interface datasets. Here, we introduce an energy-independent workflow that represents coherent interfaces as attributed graphs, quantifies their similarity to parent bulk environments using the fused Gromov-Wasserstein (FGW) distance, and couples this metric with Bayesian optimization over the in-plane registry space. We assess the approach for KI/NaCl, GaP/GaAs and GaN/$\mathrm{Al_{2}O_{3}}$ interfaces spanning ionic, covalent and mixed-bonding regimes, using hierarchical validation with MACE and density functional theory (DFT). Comparison with single-point energy landscapes shows that the FGW distance captures registry-dependent periodicity, while interfaces exhibit deviations between structural and energetic extrema, reflecting additional chemistry-specific contributions. Furthermore, FGW distances show an overall association with relaxed energies. Under limited screening budgets, FGW-guided registry selection consistently outperforms random search and is more robust across interface systems than selection guided by pretrained MACE energies. The workflow converts the qualitative notion of bulk-like continuity into a quantitative prescreening criterion, enabling efficient registry exploration and providing physically informed candidate structures for materials discovery workflows.
