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Opened by Andreas Singraber@singraberReporter3 of 19 tasks completed3/19 tasks
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WIP: Module: NNP-CG Descriptor analysis

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.
  • CI tests for tool.
  • 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

Edited by Andreas Singraber