---
title: qmof_quantum
canonical_url: "https://www.modelscope.cn/datasets/jablonkagroup/qmof_quantum"
md_url: "https://www.modelscope.cn/datasets/jablonkagroup/qmof_quantum.md"
repository: jablonkagroup/qmof_quantum
last_updated: 2025-05-27
license: "Apache License 2.0"
storage_size: "48 MB"
downloads: 937
stars: 0
---

# qmof_quantum

> qmof_quantum - jablonkagroup 在 ModelScope 开源的数据集。QMOF is a database of electronic properties of MOFs, assembled by Rosen et al. Jablonka et al. added gas adsorption properties.

jablonkagroup/qmof_quantum 是 ModelScope 魔搭社区上的数据集，存储大小 48 MB，采用 Apache License 2.0 许可。

- **Repository**: jablonkagroup/qmof_quantum
- **License**: Apache License 2.0
- **Storage size**: 48 MB
- **Downloads**: 937
- **Stars**: 0
- **Last updated**: 2025-05-27

Source: https://www.modelscope.cn/datasets/jablonkagroup/qmof_quantum

---

## Dataset Details

### Dataset Description

QMOF is a database of electronic properties of MOFs, assembled by Rosen et al.
Jablonka et al. added gas adsorption properties.

- **Curated by:**
- **License:** CC-BY-4.0

### Dataset Sources

No links provided

## Citation

<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->

**BibTeX:**

```bibtex
@article{Rosen_2021,
  doi = {10.1016/j.matt.2021.02.015},
  url = {https://doi.org/10.1016%2Fj.matt.2021.02.015},
  year = 2021,
  month = {may},
  publisher = {Elsevier {BV}},
  volume = {4},
  number = {5},
  pages = {1578--1597},
  author = {Andrew S. Rosen and Shaelyn M. Iyer and Debmalya Ray and Zhenpeng Yao and Al{\'{a}}n Aspuru-Guzik and Laura Gagliardi and Justin M. Notestein and Randall Q. Snurr},
  title = {Machine learning the quantum-chemical properties of metal{\textendash}organic frameworks for accelerated materials discovery},
  journal = {Matter}
}
@article{Rosen_2022,
  doi = {10.1038/s41524-022-00796-6},
  url = {https://doi.org/10.1038%2Fs41524-022-00796-6},
  year = 2022,
  month = {may},
  publisher = {Springer Science and Business Media {LLC}},
  volume = {8},
  number = {1},
  author = {Andrew S. Rosen and Victor Fung and Patrick Huck and Cody T. O'Donnell and Matthew K. Horton and Donald G. Truhlar and Kristin A. Persson and Justin M. Notestein and Randall Q. Snurr},
  title = {High-throughput predictions of metal{\textendash}organic framework electronic properties: theoretical challenges, graph neural networks, and data exploration},
  journal = {npj Comput Mater}
}
@article{Jablonka_2023,
  doi = {10.1021/acscentsci.2c01177},
  url = {https://doi.org/10.1021%2Facscentsci.2c01177},
  year = 2023,
  month = {mar},
  publisher = {American Chemical Society ({ACS})},
  volume = {9},
  number = {4},
  pages = {563--581},
  author = {Kevin Maik Jablonka and Andrew S. Rosen and Aditi S. Krishnapriyan and Berend Smit},
  title = {An Ecosystem for Digital Reticular Chemistry},
  journal = {ACS Cent. Sci.} Central Science}
}
```