
During January 2026, work focused on enhancing the lab-cosmo/atomistic-cookbook repository by delivering modular prefactor handling for flexible potentials. The main feature involved refactoring the codebase to move the prefactor from the EwaldCalculator to the InversePowerLawPotential, which reduced coupling and improved maintainability. This approach enabled more modular configurations and facilitated easier experimentation with different potential models. The changes were implemented using Python, leveraging skills in numerical methods and scientific computing. No major bugs were addressed during this period, but the update improved testability, clarified separation of concerns, and established a foundation for faster iteration on future enhancements.
January 2026 monthly summary for lab-cosmo/atomistic-cookbook. Key feature delivered: Modular Prefactor Handling for Flexible Potentials. By moving the prefactor from EwaldCalculator to InversePowerLawPotential, the codebase now supports more modular configurations and easier experimentation with different potential models. This change reduces coupling, improves maintainability, and sets up faster iteration for future enhancements. No major bugs fixed this month. Impact: faster onboarding for new models, clearer separation of concerns, and traceable commits. Technologies/skills demonstrated: Python refactoring, modular design, interface decoupling, testability improvements, and commit-driven development.
January 2026 monthly summary for lab-cosmo/atomistic-cookbook. Key feature delivered: Modular Prefactor Handling for Flexible Potentials. By moving the prefactor from EwaldCalculator to InversePowerLawPotential, the codebase now supports more modular configurations and easier experimentation with different potential models. This change reduces coupling, improves maintainability, and sets up faster iteration for future enhancements. No major bugs fixed this month. Impact: faster onboarding for new models, clearer separation of concerns, and traceable commits. Technologies/skills demonstrated: Python refactoring, modular design, interface decoupling, testability improvements, and commit-driven development.

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