K eff search with tally derivatives

README_PR.md

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+PR Checklist for `k_eff_search_with_tally_derivatives.ipynb`
+
+- [ ] Notebook split into logical cells with explanatory text.
+- [ ] Implementation left intact (functions: `build_model`, `run_with_gradient`, `gradient_based_search`, `builtin_keff_search`, `compare_optimization_methods`).
+- [ ] Added small experiment cells (`run_experiments`, `plot_history`) for sensitivity testing.
+- [ ] Added `requirements.txt` and `setup.sh` with recommended installation steps.
+- [ ] Added quick setup instructions in a notebook cell (call `print_setup_instructions()`).
+- [ ] Verified notebook JSON structure (cells have `metadata.language`, original cell ids preserved where applicable).
+
+Notes for reviewers:
+- OpenMC is recommended to be installed from `conda-forge`. The `setup.sh` creates a conda environment using conda-forge packages.
+- Experiments in the notebook are intentionally conservative (low batches/particles) for quick smoke tests; users should increase them for production-quality results.
+- Running the notebook will launch OpenMC simulations that produce HDF5 and statepoint files in the working directory. Consider running in a clean directory or adding `.gitignore` entries for `statepoint.*.h5`, `summary.h5`, and `tallies.out`.
+
+Suggested follow-ups before merging:
+- Add `.gitignore` entries for OpenMC outputs.
+- Add CI smoke test that runs a single quick `run_with_gradient` with very small batches (if possible in CI environment).
+- Optionally pin dependency versions in `requirements.txt` or a `environment.yml` file.