---
title: "Materials Project Trajectory (MPtrj) Dataset"
description: "3 papers in the tracked journals link to the figshare dataset Materials Project Trajectory (MPtrj) Dataset in their Crossref records, the first in 2023; most of them (1) in 2026."
url: https://scholar4ai.com/code/figshare-23713842
markdown_url: https://scholar4ai.com/code/figshare-23713842.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
share_text: "3 papers in the tracked journals link to the figshare dataset Materials Project Trajectory (MPtrj) Dataset in their Crossref records, the first in 2023; most of them (1) in 2026. (as of 2026-11-03)"
data_url: https://scholar4ai.com/data/rankings.json
updated: 2026-11-03T14:43:26+09:00
window: 2026-10-05 to 2026-11-03
source: OpenAlex (CC0)
---

# Materials Project Trajectory (MPtrj) Dataset: papers linking to it

Research papers in the 480 tracked journals that link to Materials Project Trajectory (MPtrj) Dataset (https://doi.org/10.6084/m9.figshare.23713842), read from their Crossref records: by year, category and journal, the family tree of papers building on one another, and the linked repositories derived from it or that it derives from.

- figshare
- Dataset
- 2023
- https://doi.org/10.6084/m9.figshare.23713842

All repositories: https://scholar4ai.com/code.md

## Key findings

- 3 papers in the tracked journals link to the figshare dataset Materials Project Trajectory (MPtrj) Dataset in their Crossref records, the first in 2023; most of them (1) in 2026.
- By category: AI (3); the journals with the most are Nature Machine Intelligence (3).
- 3 of the papers build on one another: a later paper cites an earlier one that also links Materials Project Trajectory (MPtrj) Dataset.

## By year

*Papers linking to Materials Project Trajectory (MPtrj) Dataset by publication year*

| Year | Papers |
|---|---:|
| 2023 | 1 |
| 2025 | 1 |
| 2026 | 1 |

## Journals

*Journals whose papers link to Materials Project Trajectory (MPtrj) Dataset most*

| Journal | Papers |
|---|---:|
| [Nature Machine Intelligence](https://scholar4ai.com/journals/nature-machine-intelligence.md) | 3 |

## Family tree

Each paper sits under the earlier paper it builds on.

- [CHGNet as a pretrained universal neural network potential for charge-informed atomistic modelling](https://doi.org/10.1038/s42256-023-00716-3) (Nature Machine Intelligence, 2023)
  - [A framework to evaluate machine learning crystal stability predictions](https://doi.org/10.1038/s42256-025-01055-1) (Nature Machine Intelligence, 2025) — cites the paper above
  - [A multimodal large language model for materials science](https://doi.org/10.1038/s42256-026-01214-y) (Nature Machine Intelligence, 2026) — cites the paper above

## Papers

*Papers linking to Materials Project Trajectory (MPtrj) Dataset, most cited first*

| Published | Title | Journal | Authors | Citations | Link |
|---|---|---|---|---:|---|
| 2023-09-14 | [CHGNet as a pretrained universal neural network potential for charge-informed atomistic modelling](https://doi.org/10.1038/s42256-023-00716-3) | [Nature Machine Intelligence](https://scholar4ai.com/journals/nature-machine-intelligence.md) | Bowen Deng, Peichen Zhong, KyuJung Jun et al. | 946 | cites it |
| 2025-06-23 | [A framework to evaluate machine learning crystal stability predictions](https://doi.org/10.1038/s42256-025-01055-1) | [Nature Machine Intelligence](https://scholar4ai.com/journals/nature-machine-intelligence.md) | Janosh Riebesell, Rhys E. A. Goodall, Philipp Benner et al. | 161 | cites it |
| 2026-04-24 | [A multimodal large language model for materials science](https://doi.org/10.1038/s42256-026-01214-y) | [Nature Machine Intelligence](https://scholar4ai.com/journals/nature-machine-intelligence.md) | Yingheng Tang, Wenbin Xu, Jie Cao et al. | 22 | cites it |

## How this is measured

Links are read from each paper's Crossref record as its publisher deposited it: the URLs and repository DOIs in its reference list (the software and data it cites) and its supplementary links (“is-supplemented-by”: its own code or data). A paper that gives its code only in the text, a footnote or a data availability statement is not counted, and some publishers deposit reference lists without their URLs, so every count here is a lower bound. Repository details come from the GitHub REST API (stars, forks, and the repository a fork was made from), the Hugging Face Hub API (likes, downloads, and the base model a model was derived from) and DataCite (Zenodo and other data DOIs, and the GitHub release a Zenodo record archives), read again every month.

## Cite this page

Scholar4AI. (2026). Materials Project Trajectory (MPtrj) Dataset: papers linking to it. Journal and Conference Research Rankings. Retrieved November 3, 2026, from https://scholar4ai.com/code/figshare-23713842

```bibtex
@misc{scholar4ai-code-figshare-23713842,
  author = {{Scholar4AI}},
  title = {Materials Project Trajectory (MPtrj) Dataset: papers linking to it},
  year = {2026},
  howpublished = {\url{https://scholar4ai.com/code/figshare-23713842}},
  urldate = {2026-11-03},
  note = {Journal and Conference Research Rankings; data from OpenAlex (CC0); licensed CC BY-NC 4.0}
}
```

License: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/).