AI 中文总结
研究人员开发了将SPR-KKR集成到ASE的Python框架ASE2SPRKKR，扩展了Atoms对象功能，支持多类材料计算，符合FAIR原则，为相关代码融入现代材料发现工作流提供了蓝图。
AI 中文摘要
自旋极化相对论Korringa-Kohn-Rostoker（SPR-KKR）是一种全电子从头算多重散射代码，其基于单粒子格林函数而非本征态的基本公式，具备处理各类固体的化学无序、有限温度磁性、相对论效应及光谱性质的独特能力。本文提出ASE2SPRKKR，这是一个将SPR-KKR集成到原子模拟环境（ASE）的综合性Python接口，使SPR-KKR更易获取、流程更精简且更统一。我们的实现扩展了ASE的Atoms对象，以处理相干势近似计算的分数位点占据，同时保持与ASE丰富的结构构建器、优化器及分析工具生态系统的完全兼容性。带验证的自动输入生成、全面的输出解析及直接的MPI支持，使其能无缝集成到高通量及多方法工作流中。我们通过代表性应用演示该接口：再现Rashba分裂Au(111)表面态的半无限表面计算；捕获矩阵元效应的一步光发射建模；原子自旋动力学的交换参数提取；包含磁圆二色性的X射线吸收光谱。除这些演示外，ASE2SPRKKR将可迁移性作为首要考量，通过基于可发现性、可访问性、互操作性及可重用性的FAIR原则构建架构，为将其他专业格林函数及从头算代码引入现代材料发现所需的协作、可复现工作流确立了可复制蓝图。
英文摘要
The Spin-Polarized Relativistic Korringa-Kohn-Rostoker (SPR-KKR) is an all-electron ab-initio multiple-scattering code that provides unique capabilities for treating chemical disorder, finite-temperature magnetism, relativistic effects, and spectroscopic properties of various types of solids through its fundamental formulation in terms of the single-particle Green's function rather than eigenstates. We present ASE2SPRKKR, a comprehensive Python interface that integrates SPR-KKR into the Atomic Simulation Environment (ASE), making SPR-KKR more accessible, streamlined, and uniform. Our implementation extends the ASE's Atoms object to handle fractional site occupations for coherent-potential-approximation calculations while maintaining full compatibility with ASE's extensive ecosystem of structure builders, optimizers, and analysis tools. Automated input generation with validation, comprehensive output parsing, and direct MPI support enable seamless integration into high-throughput and multi-method workflows. We demonstrate the interface through representative applications: semi-infinite surface calculations reproducing Rashba-split Au(111) surface states; one-step photoemission modeling capturing matrix-element effects; exchange-parameter extraction for atomistic spin dynamics; and X-ray absorption spectroscopy including magnetic circular dichroism. Beyond these demonstrations, ASE2SPRKKR is designed with transferability as a first-class concern. By grounding its architecture in FAIR principles of Findability, Accessibility, Interoperability, and Reusability, it establishes a replicable blueprint for bringing other specialized Green's function and first-principles codes into the collaborative, reproducible workflows that modern materials discovery requires.
