Editorial Policy

What qualifies as a node

Any published machine-learning interatomic potential with a public code repository is eligible. To merit its own node (versus a description inside a parent entry), a model must introduce a distinct architecture, training dataset, or use case. Minor version bumps or re-trainings are folded into their parent.

Distinct model vs. variant

If a model shares architecture and dataset with an existing entry and differs only in hyperparameters or training budget, we list it as a variant in the parent description — not as its own card. Forks with a genuinely new inductive bias or dataset get their own node.

Review cadence

Every entry carries a lastReviewed date. We aim to re-review each model at least once every 12 months, plus on any reported broken link or significant upstream change. Entries older than 12 months without a review surface in the coverage table below as candidates for refresh.

What “maintained” means

• active — commits, releases, or issue responses in the last 6 months.
• maintained — receives bug fixes and compatibility updates but no new features.
• archived — repository frozen or marked archived upstream.
• experimental — research code, may break; no maintenance commitment.

Corrections & additions

To report a broken link, stale description, or request a new model, open an issue on the GitHub repository or email support@mliphub.com. See Contribute for the full workflow.