SPARC-X-API: Versatile Python Interface for Real-space Density Functional Theory Calculations
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.100 | 0.056 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".