# MPtrj

The Materials Project contains a large set of relaxation data across its entire database. These relaxation "trajectories," along with single-point data, for the PBE GGA and GGA+U calculations are stored in the [tasks](https://docs.materialsproject.org/frequently-asked-questions#what-is-a-task_id-and-what-is-a-material_id-and-how-do-they-differ) collection of Materials Project. Deng *et al.* collated the energies, forces, stresses, structures, and on-site magnetic moments (when available) from this ionic step data, and used it to train a graph neural network, CHGNet \[1], for the potential energy surface of most of the periodic table.

The collected dataset is called *MPtrj* in the [CHGNet](https://chgnet.lbl.gov/) \[1]. Subsets of MPtrj were used in the earlier universal machine learning interatomic potentials (MLIPs) [M3GNet](https://matgl.ai/) \[2] and later ones such as MACE-MP-0 \[3].

The original MPtrj dataset is hosted on MPContribs ([explorer](https://next-gen.materialsproject.org/contribs/projects/MPtrj_2022_9)) ([bulk download](https://s3.us-east-1.amazonaws.com/materialsproject-contribs/MPtrj_2022_9/MPtrj-2022.9_full.parquet)) with a parquet format bulk download. An example notebook for working with parquet data is also [available](https://s3.us-east-1.amazonaws.com/materialsproject-contribs/MatPES_2025_1/working_with_parquet_data.ipynb).

### References

\[1] B. Deng, P. Zhong, K.J. Jun, J. Riebesell, K. Han, C. J. Bartel, and G. Ceder. "CHGNet as a pretrained universal neural network potential for charge-informed atomistic modelling". *Nature Mach. Intell.*, vol. 5, pp. 1031–1041, yr. 2023. ([DOI](https://doi.org/10.1038/s42256-023-00716-3)) ([original figshare](https://figshare.com/articles/dataset/Materials_Project_Trjectory_MPtrj_Dataset/23713842?file=41619375))

\[2] C. Chen and S.P. Ong, "A universal graph deep learning interatomic potential for the periodic table". *Nature Comput. Sci.*, vol. 2, pp. 718–728, yr. 2022. ([DOI](https://doi.org/10.1038/s43588-022-00349-3))

\[3] I. Batatia *et al.*, "A foundation model for atomistic materials chemistry". arXiv:2401.00096v3, yr. 2025. ([DOI](https://doi.org/10.48550/arXiv.2401.00096))


---

# 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/mptrj.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.