# r2SCAN datasets

## MatPES

The materials potential energy surface (MatPES) collaboration aims to generate low noise, high coverage small datasets of DFT-computed properties (energies, forces, stresses, magnetic moments, etc.) for training universal machine learning interatomic potentials.

The data is generated with the [`MatPESStaticSet`](https://github.com/materialsproject/pymatgen/blob/f9d9fe8e0ce09ef30cc03bcc4e9937d27afd5a6a/src/pymatgen/io/vasp/sets.py#L1655) in pymatgen, with efficient PBE and r<sup>2</sup>SCAN workflows implemented in [atomate2](https://github.com/materialsproject/atomate2/blob/457f017fd82ecfb67aec10c794600874bfbbeaf7/src/atomate2/vasp/flows/matpes.py#L25).

The full dataset can be[ downloaded from MPContribs](https://materialsproject-contribs.s3.amazonaws.com/index.html#MatPES_2025_1/) and uses the [MatPESTrainDoc schema](https://github.com/materialsproject/emmet/blob/56840ac7110096636565809cd72036fbb064392e/emmet-core/emmet/core/ml.py#L393) from `emmet-core`

## MP-ALOE

In a similar vein, Kuner *et al.* \[2] sought to expand both the size and chemistries chosen in an r<sup>2</sup>SCAN dataset, and used an active learning method to explore under-sampled regions of the potential energy surface. The resultant Materials Project active learning of off-equilibrium structures (MP-ALOE) dataset contains \~900,000 r<sup>2</sup>SCAN calculations which are compatible with MatPES.

**\[WIP]** MP-ALOE will similarly be available on MPContribs (explorer) (bulk download).

## References:

\[1] A. D. Kaplan, R. Liu, J. Qi, T. W. Ko, B. Deng, J. Riebesell, G. Ceder, K. A. Persson, and S. P. Ong, “A\
foundational potential energy surface dataset for materials,” arXiv:2503.04070, yr. 2025 ([DOI](https://doi.org/10.48550/arXiv.2503.04070)) ([MPContribs explorer](https://next-gen.materialsproject.org/contribs/projects/MatPES_2025_1)) ([website](https://matpes.ai/))

\[2] M.C. Kuner, A.D. Kaplan, K.A. Persson, M. Asta, and D.C. Chrzan, "MP-ALOE: An r<sup>2</sup>SCAN dataset for universal machine learning interatomic potentials". arXiv:2507.05559, yr. 2025. ([DOI](https://doi.org/10.48550/arXiv.2507.05559)) ([original figshare](https://doi.org/10.6084/m9.figshare.29452190.v2))


---

# Agent Instructions: Querying This Documentation

If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
GET https://docs.materialsproject.org/services/ml-and-ai-applications/matpes.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.