
During March 2025, Bananenpampe updated the PET-MAD documentation in the lab-cosmo/pet-mad repository to support integration with the Atomic Simulation Environment (ASE) and empirical dispersion corrections. They provided clear installation instructions for torch-dftd and included a Python code snippet demonstrating how to combine PET-MAD with D3 dispersion calculators, streamlining the workflow for scientific users. This work, implemented using Python, Bash, and Markdown, focused on improving onboarding and reproducibility for ASE-based PET-MAD workflows. The update addressed practical adoption challenges, reducing setup friction for new users and enabling more reliable, dispersion-corrected calculations in computational chemistry research environments.

March 2025 monthly summary for lab-cosmo/pet-mad. Key feature delivered: PET-MAD documentation updated to enable usage with ASE and empirical dispersion corrections, including installation guidance for torch-dftd and a Python snippet that demonstrates combining PET-MAD with D3 dispersion calculators. No major bugs reported this month. Overall impact: improves onboarding, reproducibility, and practical adoption of PET-MAD in ASE-based workflows, accelerating scientific experiments and reliable dispersion-corrected calculations. Technologies and skills demonstrated: documentation best practices, Python snippet creation, ASE/dispersions workflows integration concepts, and familiarity with torch-dftd and D3 calculators.
March 2025 monthly summary for lab-cosmo/pet-mad. Key feature delivered: PET-MAD documentation updated to enable usage with ASE and empirical dispersion corrections, including installation guidance for torch-dftd and a Python snippet that demonstrates combining PET-MAD with D3 dispersion calculators. No major bugs reported this month. Overall impact: improves onboarding, reproducibility, and practical adoption of PET-MAD in ASE-based workflows, accelerating scientific experiments and reliable dispersion-corrected calculations. Technologies and skills demonstrated: documentation best practices, Python snippet creation, ASE/dispersions workflows integration concepts, and familiarity with torch-dftd and D3 calculators.
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