标题： SPARC-X-API：用于实空间密度泛函理论计算的通用Python接口 显示英文标题

标题： SPARC-X-API: Versatile Python Interface for Real-space Density Functional Theory Calculations

摘要： 密度泛函理论（DFT）是化学和材料科学中大规模电子结构计算的默认工具。虽然平面波DFT实现仍然是最广泛使用的，但实空间DFT通过允许高效使用大规模超级计算机和规避立方级数瓶颈的线性级数方法，在处理复杂边界条件和扩展到非常大的系统方面具有优势。SPARC-X项目（https://github.com/SPARC-X）为广泛的首次原理应用提供了高效且可移植的实空间DFT代码，分别以Matlab（M-SPARC）和C/C++（SPARC）形式提供。SPARC功能集的快速增长促使需要一个完整的接口来在高通量计算中驱动SPARC。在此我们介绍SPARC-X-API，这是一个Python包，旨在将SPARC-X项目与更广泛的计算框架连接起来。基于原子模拟环境（ASE）标准，SPARC-X-API允许用户处理SPARC文件格式并通过与其他ASE兼容的DFT软件相同的接口运行SPARC计算。除了标准的ASE功能外，SPARC-X-API还提供了其他功能，包括1）支持SPARC特定的设置，包括复杂的边界条件和单位转换，2）从SPARC文档解析的JSON模式用于参数验证和兼容性检查，以及3）从i-PI协议派生的全面的套接字通信层，促进低级C代码和Python接口之间的消息传递。SPARC-X-API的目标是为具有不同需求和专业知识水平的用户提供易于使用的接口，使适应SPARC到现有计算工作流的努力最小化，同时支持先进实空间方法的开发者。 显示英文摘要

摘要： Density Functional Theory (DFT) is the de facto workhorse for large-scale electronic structure calculations in chemistry and materials science. While plane-wave DFT implementations remain the most widely used, real-space DFT provides advantages in handling complex boundary conditions and scaling to very large systems by allowing for the efficient use of large-scale supercomputers and linear-scaling methods that circumvent the cubic scaling bottleneck. The SPARC-X project (https://github.com/SPARC-X) provides highly efficient and portable real-space DFT codes for a wide range of first principle applications, available in both Matlab (M-SPARC) and C/C++ (SPARC). The rapid growth of SPARC's feature set has created the need for a fully functional interface to drive SPARC in high-throughput calculations. Here we introduce SPARC-X-API, a Python package designed to bridge the SPARC-X project with broader computational frameworks. Built on the atomic simulation environment (ASE) standard, the SPARC-X-API allows users to handle SPARC file formats and run SPARC calculations through the same interface as with other ASE-compatible DFT packages. Beyond standard ASE capabilities, SPARC-X-API provides additional features including 1) support of SPARC-specific setups, including complex boundary conditions and unit conversion, 2) a JSON schema parsed from SPARC's documentation for parameter validation and compatibility checks, and 3) a comprehensive socket communication layer derived from the i-PI protocol facilitating message passing between low-level C code and the Python interface. The goal of the SPARC-X-API is to provide an easy-to-use interface for users with diverse needs and levels of expertise, allowing for minimal effort in adapting SPARC to existing computational workflows, while also supporting developers of advanced real-space methods.