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 **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} } ```## 数据集详情 ### 数据集描述 QMOF是金属有机框架（Metal-Organic Frameworks, MOFs）的电子性质数据库，由Rosen等人搭建。Jablonka等人后续补充了气体吸附相关性质。 - **整理方**： - **授权协议**：CC-BY-4.0 ### 数据集来源 未提供相关链接 ### 引用信息 **BibTeX格式引用：** 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 Alán Aspuru-Guzik and Laura Gagliardi and Justin M. Notestein and Randall Q. Snurr}, title = {用于加速材料发现的金属有机框架量子化学性质机器学习研究}, 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 = {金属有机框架电子性质的高通量预测：理论挑战、图神经网络（Graph Neural Networks）与数据探索}, 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 = {数字网状化学研究生态系统}, journal = {ACS Cent. Sci.} Central Science} }

搜集汇总

背景与挑战

背景概述

QMOF是一个金属有机框架（MOFs）电子性质数据库，由Rosen等人汇编，Jablonka等人补充了气体吸附性质。该数据集采用CC-BY-4.0许可证。