September 2025 performance summary for lab-cosmo projects: Atomistic Cookbook and Pet-Mad advanced core capabilities, reliability, and maintainability with a focus on accurate rotational analytics, SciPy-based workflows, and scalable performance.

July 2025: Delivered core PET-MAD enhancements while stabilizing the codebase. Key outcomes include integrating uncertainty quantification (UQ) into the PET-MAD calculator and releasing v1.2.0rc2, activating a non-conservative regime with an accompanying preprint link, expanding the test suite, and making targeted documentation/versioning improvements to improve maintainability. Stabilization work included deactivating UQ due to issues and temporarily rolling back recent HF changes to restore stability. Ongoing repository hygiene efforts reduced noise and clarified release tagging. Business value: (1) enhanced analytical capability and faster time-to-insight through UQ-in-PET-MAD; (2) reduced production risk via stabilization work and regression testing; (3) clearer release management and maintainability via improved versioning/docs and linting. Technologies/skills demonstrated: Python-based analytics integration, test automation, code quality (linting), versioning and repository metadata, release tagging, and documentation practices.

June 2025: Stabilized PET model training in metatensor/metatrain by fixing a DataLoader argument type error. Replaced incorrect 'dataset' with 'train_dataset' to ensure training runs on the intended data, restoring the correct training workflow and preventing runtime derailments. This change improves data integrity, training reproducibility, and overall project velocity.

May 2025 monthly highlights for lab-cosmo/pet-mad focusing on GPU deployment readiness, documentation quality, and release management. Delivered user-facing KOKKOS GPU usage guidance for LAMMPS-METATENSOR, including GPU compute capability identification and conda install commands for diverse MPI implementations and GPU architectures; updated an example LAMMPS input script to reflect the guidance. Completed thorough documentation cleanup and readability improvements across PET-MAD/LAMMPS docs, with README reorganization, new subsections, cosmetic formatting, and a README rename to clarify building LAMMPS workflows. Established a formal PET-MAD release cycle by updating versioning to 1.1.0 across core, docs, and packaging, including fixes to version labels. Minor quality improvements included a linting fix and targeted cosmetic tweaks in docs. These efforts reduce onboarding time, enable smoother GPU-accelerated usage, and provide a reliable, reproducible release baseline.

April 2025 – PET-MAD development and release readiness. Delivered PET-MAD v1.1 with a PyTorch backend, performance improvements, and non-conservative forces/stresses support, plus migration guidance and deprecation of older versions. Strengthened compatibility through versioning changes and expanded tests for deprecated configurations; improved release hygiene with RC bumps and packaging metadata cleanup. Progress on non-conservative readiness: parameter exposure, test adjustments, and documentation alignment for safe rollout. Code quality and user-facing improvements: lint fixes, clearer messaging around model versions, GPU usage, installation, and licensing metadata updates. Overall, these efforts boost production readiness, broaden configuration support, and reduce onboarding time for new users.

March 2025: Delivered PET-MAD from initial concept to production-ready release with a solid core, end-user setup, build and documentation foundations, and evaluation tooling. Focused on onboarding, reproducibility, and scalable packaging to enable rapid experimentation across environments.