This module adds tools to the N2P2 package which allow to assess the quality of atomic environment descriptors. This is particularly useful when designing a neural network potential based coarse-grained model (NNP-CG).

Roadmap

• Create new tool to collect descriptor data for external processing.
• Design and write Python (Jupyter) tools to analyse descriptor quality.
• Provide example.
• Source code documentation.
• Complete module README.

Module verification checklist (for reviewers)

Checklist when the module is first submitted

• Have the relevant labels been added to the MR
• If submitted on someone elses behalf, has the software author been referenced (if they have a GitLab account)

Checklist when module is no longer "WIP"

• Is the module documentation sufficiently detailed?
• Is it mergeable? (i.e., there should be no merge conflicts)
• Are the build instructions sufficient - source code locations, build instructions, etc.? (If not the MR should be updated)
• Did it pass the tests that were described? (Are there unit/regression tests? Do they pass?)
• Are the tests sufficient?
• If the module introduces new functionality, is it tested? (Unit/regression tests?)
• Is the associated source code well formatted? (typos, line length, brackets,...it should be consistent with existing source)
• Is all new source code sufficiently documented? (functions, their arguments,...)
• Is there a description of any applications the module has? (This is a hard requirement for E-CAM PDRAs)

After Merging

• Make sure the module appears in a toctree
• Add a link to the final result on https://e-cam.readthedocs.io