MolPy is developed as a scientific modeling framework. Its primary responsibility is to support physically meaningful, reproducible, and methodologically sound simulations.

This policy concerns the identification and handling of critical defects that may compromise:

• Scientific correctness (e.g. incorrect physical models, flawed algorithms, invalid assumptions)

• Numerical validity (e.g. unstable schemes, hidden unit inconsistencies, silent numerical errors)

• Reproducibility (e.g. undocumented sources of non-determinism, inconsistent defaults)

• Data and topology integrity (e.g. incorrect atom mappings, broken connectivity, invalid force-field assignments)

• Interpretability of results (e.g. misleading outputs that appear plausible but are scientifically incorrect)

This policy does not apply to feature requests, performance optimizations, or minor usability issues.

Please do not report critical scientific or methodological issues via public issue trackers.

If you identify a fatal bug, methodological flaw, or scientific inconsistency, please contact the maintainers privately at:

📧 [email protected]

A report should include, where possible:

1. Description of the issue A clear explanation of what is incorrect and why it is scientifically or methodologically significant.

2. Affected components For example: structure builders, reaction templates, force-field handling, analysis workflows, or numerical routines.

3. Reproduction or evidence

   • Minimal scripts or configurations
   • Counterexamples
   • Analytical arguments
   • Comparisons with reference implementations or literature

4. Observed vs expected behavior Especially important for numerical or physical discrepancies.

5. Scientific impact Including whether:

   • Results may be quantitatively wrong
   • Qualitative trends may be misleading
   • Published or shared conclusions could be affected

6. Suggested correction or references (optional) Patches, alternative formulations, or relevant literature are welcome but not required.

All reported issues will be evaluated with respect to:

• Severity and scope of scientific impact
• Whether the issue is conceptual, algorithmic, or implementation-related
• Potential implications for reproducibility and prior results

Depending on the outcome, responses may include:

• Code corrections or refactoring
• Explicit documentation of limitations or assumptions
• Deprecation of affected functionality
• Release notes or technical advisories describing the issue and its resolution

MolPy follows a science-first disclosure principle:

• Scientifically significant issues will be documented once understood
• Corrections will be released together with clear explanations
• If prior results may be affected, this will be stated explicitly

Our objective is not to obscure errors, but to ensure that users can evaluate, reproduce, and trust the results produced with the framework.

Users of MolPy are encouraged to:

• Validate results against physical intuition and reference methods
• Inspect assumptions and defaults used in workflows
• Treat automated outputs as scientific hypotheses, not ground truth
• Report suspected inconsistencies, even when uncertain

MolPy is intended to be a transparent and inspectable scientific tool, not a black box. We consider the identification and correction of critical bugs and scientific errors to be a core part of responsible research software development.

We appreciate the community’s role in maintaining scientific rigor and reproducibility.