deepmodeling/abacus-develop
Jun 2026 – Jun 2026
1 Month active
Languages Used
Python
Technical Skills
Python scriptingdata analysisdata processingfile handlingscientific computing
Jiacheng Xu
PROFILE
Jiacheng Xu
Worked on the deepmodeling/abacus-develop repository to enhance wavefunction processing by improving Molden output and integrating with the Multiwfn interface. Focused on Python scripting and scientific computing, the work introduced normalization fixes, coordinate conversion, and optional multi-start NAO-to-GTO fitting to increase robustness and fit quality. The Molden converter was refactored to streamline analytics across tools, while an option to export valence data was added for greater flexibility. All enhancements were validated through unit tests, ensuring default behaviors remained stable. These updates reduced manual data handling and improved the reliability and interoperability of quantum analysis workflows and downstream processing.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
2Total
Bugs
0
Commits
2
Features
1
Lines of code
285
Activity Months1
Your Network
32 peopleWork History
June 2026
2 Commits • 1 Features
Jun 1, 2026June 2026 monthly summary for deepmodeling/abacus-develop focused on delivering robust wavefunction processing enhancements and strengthening cross-tool interoperability. Key work includes Molden output improvements, integration with Multiwfn, and groundwork for more robust NAO-to-GTO fitting, while preserving existing default behavior. The work reduces manual data handling, improves analysis reliability, and enables smoother downstream workflows in quantum analysis pipelines.
2 Commits • 1 Features
Jun 1, 2026June 2026 monthly summary for deepmodeling/abacus-develop focused on delivering robust wavefunction processing enhancements and strengthening cross-tool interoperability. Key work includes Molden output improvements, integration with Multiwfn, and groundwork for more robust NAO-to-GTO fitting, while preserving existing default behavior. The work reduces manual data handling, improves analysis reliability, and enables smoother downstream workflows in quantum analysis pipelines.
June 2026
Activity
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Quality Metrics
Correctness90.0%
Maintainability80.0%
Architecture80.0%
Performance80.0%
AI Usage40.0%
Skills & Technologies
Programming Languages
Python
Technical Skills
Python scriptingdata analysisdata processingfile handlingscientific computing
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