AI 中文总结
研究复杂多元材料团簇展开时训练成本问题，提出在主动学习工作流程中集成稳定性分类，用基于马氏距离的结构选择增强该程序，通过训练复杂FCC MPEA团簇展开进行基准测试，确保模型稳健性。
AI 中文摘要
团簇展开的高效性使其在研究复杂成分空间（如多主元合金（MPEA））中的化学无序方面具有吸引力。一些工作试图通过主动学习、迁移学习和化学嵌入来解决随着化学物种数量快速增长的训练成本问题。然而，许多成分空间存在宿主晶格变得动力学不稳定的大区域，通常会先验地避免这些区域以不生成昂贵但不适用的训练数据。在此，我们展示了一种在主动学习工作流程中集成稳定性分类的程序，以自主避免对不稳定结构的计算。我们的工作流程通过基于马氏距离的结构选择增强稳定性分类程序，以通过训练集多样化确保模型稳健性。我们通过为跨越Ni-Fe-Cr-Al-Ti-Si合金空间的复杂面心立方MPEA训练团簇展开来对我们的方法进行基准测试，其中只有Ni和Al作为面心立方是热力学稳定的。
英文摘要
The high efficiency of cluster expansions make them appealing for studying chemical disorder in complex composition spaces such as in multi-principal element alloys (MPEAs). Several works have attempted to address the rapidly growing training cost with number of chemical species through active learning, transfer learning, and chemical embedding. However, many composition spaces have large regions where the host lattice becomes dynamical unstable, which are often avoided a priori so as to not generate expensive but inapplicable training data. Here, we demonstrate a procedure for integrating stability classification within an active learning workflow to autonomously avoid calculations for unstable structures. Our workflow augments the stability classification procedure with Mahalanobis distance-based structure selection to ensure model robustness by training set diversification. We benchmark our methods by training a cluster expansion for the complex FCC MPEA spanning the Ni-Fe-Cr-Al-Ti-Si alloy space, in which only Ni and Al are thermodynamically stable as FCC
Comments13 pages, 8 figures, supporting information included
