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

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 (Xu et al., 2020;Zhang et al., 2023)) and C/C++ (SPARC (Xu et al., 2021;Zhang et al., 2024)).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 (Hjorth Larsen et al., 2017)) 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 (Ceriotti et al., 2014;Kapil et al., 2019) 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.