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

figshare · Dataset · 2023 · doi.org/10.6084/m9.figshare.23713842

← Code and data repositories cited in research papers

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.

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#

20232026

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	3

Family tree#

Each paper sits under the earlier paper it builds on; papers that cite no other paper here start a branch of their own.

CHGNet as a pretrained universal neural network potential for charge-informed atomistic modelling Nature Machine Intelligence, 2023
- A framework to evaluate machine learning crystal stability predictions Nature Machine Intelligence, 2025 — cites the paper above
- A multimodal large language model for materials science Nature Machine Intelligence, 2026 — cites the paper above

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	Nature Machine Intelligence	Bowen Deng, Peichen Zhong, KyuJung Jun et al.	946	cites it
2025-06-23	A framework to evaluate machine learning crystal stability predictions	Nature Machine Intelligence	Janosh Riebesell, Rhys E. A. Goodall, Philipp Benner et al.	161	cites it
2026-04-24	A multimodal large language model for materials science	Nature Machine Intelligence	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

@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}
}

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