Submit rgpycrumbs for review

[HaoZeke]

Submitting Author: Rohit Goswami (@HaoZeke)
All current maintainers: (@HaoZeke)
Package Name: rgpycrumbs
One-Line Description of Package: A dispatcher-managed analytical engine for computational chemistry -- kernel-based surface fitting, structural analysis, and reactive path projection.
Repository Link: https://github.com/HaoZeke/rgpycrumbs
Version submitted: v1.1.0
EiC: @kysolvik
Editor: TBD
Reviewer 1: TBD
Reviewer 2: TBD
Archive: TBD
JOSS DOI: TBD
Version accepted: TBD
Date accepted (month/day/year): TBD

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Description

rgpycrumbs provides a modular analytical framework for chemical physics and molecular kinetics. The library implements:

• Kernel-based surface fitting (rgpycrumbs.surfaces): JAX-based Gaussian process regression with Matern 3/2, inverse multiquadric, rational quadratic, and squared exponential kernels. Includes gradient-enhanced and Nystrom-approximated variants for interpolating 2D energy/eigenvalue landscapes from sparse NEB data.
• Structural analysis (rgpycrumbs.geom): Fragment detection via Wiberg Bond Orders, Iterative Rotations and Assignments (IRA) for structure matching, and RMSD-based reactive path projection into 2D manifolds.
• Hermite spline interpolation (rgpycrumbs.interpolation): Force-consistent spline interpolation of energy profiles along reaction coordinates.
• Shared data types (rgpycrumbs.basetypes): Standardized representations for NEB paths and molecular structures.
• PEP 723 dispatcher: An execution "launchpad" that manages isolated environments for CLI scripts with conflicting binary dependencies (e.g. OVITO for defect analysis alongside tblite for electronic structure).

The companion library chemparseplot (chemparseplot>=1.0.0) handles parsing outputs from computational chemistry codes (eOn, ORCA, ChemGP) and delegates heavy computation to rgpycrumbs for publication-quality visualizations.

Scope

• Data retrieval
• Data extraction
• Data processing/munging
• Data deposition
• Data validation and testing
• Data visualization¹
• Workflow automation
• Citation management and bibliometrics
• Scientific software wrappers
• Database interoperability

Domain Specific

• Geospatial
• Education

Community Partnerships

• Astropy: My package adheres to Astropy community standards
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• For all submissions, explain how and why the package falls under the categories you indicated above:

  Who is the target audience and what are the scientific applications?

  Computational chemists and physicists investigating reaction rates, saddle point searches, and long-timescale molecular dynamics. The software provides the implementation foundation for the doctoral dissertation "Efficient Exploration of Chemical Kinetics" (University of Iceland, 2025, arXiv: 2510.21368) and supports GP-accelerated saddle point searches published in ChemPhysChem (doi: 10.1002/cphc.202500730).

  Are there other Python packages that accomplish the same thing?

  General libraries like ASE or pymatgen provide atomic simulation environments but lack: (1) the 2D reactive path RMSD projection method, (2) gradient-enhanced kernel interpolation for sparse NEB landscapes, (3) environmental isolation for heterogeneous binary dependencies. rgpycrumbs complements these tools rather than replacing them -- it uses ASE's Atoms objects as its native data type.

  Pre-submission inquiry:

  pyOpenSci/software-submission#275 -- accepted with submission-requested tag by @kysolvik.

  External adoption:

  • eOn -- long-timescale molecular dynamics code; uses rgpycrumbs as its companion
    diagnostic and visualization suite (https://eondocs.org/)
  • lab-cosmo cookbook -- community cookbook for atomistic simulations; the
    https://atomistic-cookbook.org/examples/eon-pet-neb/eon-pet-neb.html imports rgpycrumbs.eon.helpers and rgpycrumbs.run.jupyter
  • metatensor -- uses rgpycrumbs for eOn NEB plotting and PLUMED FES
    reconstruction
  • Five reproduction packages for published/submitted work.
  • Carpentries-style teaching materials
    ([Review]: Python Dissemination carpentries-lab/reviews#35)

  Full list maintained at https://rgpycrumbs.rgoswami.me/used_by.html. Documentation uses an org-to-rst pipeline with Sphinx
  (https://rgpycrumbs.rgoswami.me).

Technical checks

For details about the pyOpenSci packaging requirements, see our packaging guide. Confirm each of the following by checking the box. This package:

• does not violate the Terms of Service of any service it interacts with.
• uses an OSI approved license (MIT).
• contains a README with instructions for installing the development version.
• includes documentation with examples for all functions.
• contains a tutorial with examples of its essential functions and uses.
• has a test suite.
• has continuous integration setup, such as GitHub Actions CircleCI, and/or others.

Generative AI Disclosure

Generative AI (Claude, Anthropic) was used as an agentic coding assistant throughout development of rgpycrumbs and the companion library chemparseplot. Specific usage:

• Components: All modules received some degree of AI-assisted development, including surface fitting kernels, test suites, CLI scripts, and documentation.

• Scale: AI assistance ranged from line-level completions to full function implementations. The mathematical core (kernel definitions, gradient derivations, RMSD projections) was specified by the human author with AI implementing the numerical code. Test suites were largely AI-generated from human-specified test cases and reference values.

• Methods: Agentic workflows (Claude Code CLI) for multi-file refactoring, code generation from specifications, and test writing. Interactive queries for algorithm design and debugging.

• Human review: All generated material has been reviewed and edited for clarity, correctness, and completeness. Mathematical implementations were validated against published formulas and reference implementations (MATLAB, Julia). Test suites run in CI on every commit.

  To clarify the development history: the core algorithms derive from https://github.com/TheochemUI/gpr_optim, a C++ port of
  Oli-Pekka Koistinen's original MATLAB GPR-dimer code, written by hand with Satish Kamath and Maxim Masterov (est. 2020). The kernel
  mathematics, gradient derivations, and RMSD projection methods were developed and validated against these references over the
  course of doctoral dissertation work (2020--2025). The reproduction packages (https://github.com/TheochemUI/gpr_sella_repro,
  https://github.com/TheochemUI/otgpd_repro, https://github.com/HaoZeke/nebmmf_repro, https://github.com/HaoZeke/nebviz_repro,
  https://github.com/HaoZeke/brms_idrot_repro) predate any AI-assisted development. The package architecture (PEP 723 dispatcher,
  org-to-rst documentation pipeline, Sphinx integration) is original design work.

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• I expect to maintain this package for at least 2 years and can help find a replacement for the maintainer (team) if needed.

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[kysolvik]

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Editor comments

Hi again @HaoZeke! Thanks for submitting rgpycrumbs. Here are the EiC checks. Most of them are met, just a few things before we move forward (2-4 are the main things for now):

1. This submission is just for rgpycrumbs and does not include chemparseplot, correct?
2. Documentation: Some functions (e.g. muller_brown, spline_interp, negative_all_matern_std) are missing docstrings or missing details in their docstrings, e.g. info on arguments and returns. In addition, some sub-modules/classes/methods that are denoted for internal use (e.g. rgpycrumbs.surfaces._kernels and rgpycrumbs.eon._mlflow) are included in the API reference, but without detailed documentation. Could you clean up the API reference to either remove these or add documentation for user-facing members? For example, the kernels provided by rgpycrumbs.surfaces._kernels. Might need to tweak your Sphinx/AutoAPI options.
3. Quick start tutorials: The README has install instructions and some quick-start examples, but I couldn't find them in the docs. Might be worth adding a quickstart/getting started page there to help users orient themselves before pointing to the various tools?
4. Testing/Linting: It looks like the last linting run failed. Also, would be helpful to include a badge for testing state in the README. pyOpenSci has some README criteria in the packaging guide.
5. Would you consider adding an [all] or [full] dependency spec to the pyproject.toml? This would make it easier for users who want to use the full functionality, rather than separately installing say rgpycrumbs[analysis] and rgpycrumbs[surfaces]. This isn't a requirement and maybe it doesn't make sense in this case, but just an idea.
6. Regarding the JOSS submission, there doesn't seem to be a paper.md or a DOI from a long-term repository (e.g. Zenodo). You can add these at a later date during the review process, but just a heads up that they would be required for consideration by JOSS.

Let me know when you've had the chance to address these and then we can move on to the next step (finding an editor to see rgpycrumbs through the rest of the process).

[HaoZeke]

Hi @kysolvik, thanks for the quick turn around! I updated rgpycrumbs to v1.2.0 and addressed all the comments, with a point by point response below:

This submission is just for rgpycrumbs and does not include chemparseplot, correct?

At the moment, yes, but mostly because I was unclear on the guidelines of submitting two packages which are joint, I'm happy to add chemparseplot to the same submission, but I'll need some time (~ few days) to update the documentation to be at parity with rgypcrumbs.

Documentation ...

Yup, these have now been docstring'd so they show up correctly, and with [source] links as well (e.g. The Muller Brown)

Quick start tutorials..

Great point, I've added an explicit Quickstart and reworked the documentation to adopt the (relatively new) https://onegoodtutorial.org/ guidelines.

Testing/Linting..

These are now passing on CI, and badges have been added.

Would you consider adding an [all] or [full] dependency spec to the pyproject.toml?...

This was a very interesting thing to consider. I ended up with both an all spec, and a "lazy" loading environment variable setup for dynamic dependency resolution through uv as well, detailed in the architecture notes.

Of course pixi deps can't be handled through either, but I think that's OK.

Regarding the JOSS submission...

Sorry about that, I started working on the paper.md on an orphan branch, paper/joss and will be ready soonish (definitely before the review process is underway).

Let me know if things look good to move forward

[kysolvik]

Hi @HaoZeke!

Thanks for your response! All looks good to me. Thanks for pointing out onegoodtutorial, that's an awesome resource and the new quickstart looks great.

For chemparseplot, I'm also unsure of how we'd handle two packages in a single review. Unless you feel strongly that you want them both reviewed simultaneously, I'd suggest we go forward with rgpycrumbs as a standalone submission for now.

There's no big rush for the paper.md to be complete, just needs to be done before the review is finished. Let me know whenever it's ready I'll go back and check that box.

Next step is to find an editor to shepherd rgpycrumbs through the rest of the process. We're working our way through a backlog right now so that might take a little while, but I'll keep you posted on the progress. In the meantime let me know if you have any questions!

[HaoZeke]

Thanks for your response! All looks good to me. Thanks for pointing out onegoodtutorial, that's an awesome resource and the new quickstart looks great.

Wonderful, yeah it's really neat.

For chemparseplot, I'm also unsure of how we'd handle two packages in a single review. Unless you feel strongly that you want them both reviewed simultaneously, I'd suggest we go forward with rgpycrumbs as a standalone submission for now.

Yeah I think going with standalone rgpycrumbs makes perfect sense.

There's no big rush for the paper.md to be complete, just needs to be done before the review is finished. Let me know whenever it's ready I'll go back and check that box.

Sure, will do.

Next step is to find an editor to shepherd rgpycrumbs through the rest of the process. We're working our way through a backlog right now so that might take a little while, but I'll keep you posted on the progress. In the meantime let me know if you have any questions!

No worries, and thanks, looking forward to it!