JOSS Review Comments - Reviewer 1

[amkrajewski]

Below are initial (itemized) JOSS review comments from @Henrium. I will progressively work on addressing them one-by-one here.

1. I have tested in both GitHub Codespaces and Linux, the package is easy to install and works as claimed.

2. Summary: I suggest the following to make it more accessible to "diverse, non-specialist audience": (1) introduce the background first, then what nimCSO is and what it does; (2) elaborate on the purpose and challenges.

3. State of field: What are some other approaches to compositional space optimization; are there relevant software? References should be added if applicable. It's not necessary to compare with them, but good to make the paper informative.

4. In quickstart.ipynb: the routine mostCommon is clear at first, but got confusing when it comes to "removing elements". What's the optimization objective of removing elements?

5. The "Algorithm-Based Search" method relies on an assumption, "elements present in already expanded ...", is it supported by any rationale, experiments, prior studies, etc.?

6. I didn't find "community guidelines", though it doesn't seem necessary here. Consider adding one?

[RMeli]

https://github.com/openjournals/joss-reviews/issues/6731

[RMeli]

@amkrajewski could you please briefly summarise the progress on the items listed here? Thank you.

[amkrajewski]

• [x] Improvement of the summary to make it more approachable to the general audience.
• [ ] Elaborate on other methods - reference the Python and NumPy implementations presented within the benchmarks directory.
• [x] Add more references to other scientific domains to broadly present the problem.
• [ ] Clarify "removal of elements" in the quick start guide.
• [x] Elaborate on the rationale for algorithm assumption.
• [x] Add "community guidelines" on how to contribute and what.