qm8

## Dataset Details

### Dataset Description

QM8 is a dataset of quantum mechanical simulations of electronic spectra
and the energy levels of excited states in small molecules.
The dataset involves the application of various techniques,
such as time-dependent density functional theories (TDDFT)
and second-order approximate coupled-cluster (CC2),
to a group of molecules, which encompasses those containing as many as eight heavy atoms.
These molecules also form a subset of the GDB-17 database.

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

### Dataset Sources

- [original dataset](https://deepchemdata.s3-us-west-1.amazonaws.com/datasets/gdb8.tar.gz)

## Citation

**BibTeX:**

```bibtex
@article{Blum_2009,
doi = {10.1021/ja902302h},
url = {https://doi.org/10.1021%2Fja902302h},
year = 2009,
month = {jun},
publisher = {American Chemical Society ({ACS})},
volume = {131},
number = {25},
pages = {8732--8733},
author = {Lorenz C. Blum and Jean-Louis Reymond},
title = {970 Million Druglike Small Molecules for
Virtual Screening in the Chemical Universe Database {GDB}-13},
journal = {J. Am. Chem. Soc.}
}
@article{Ramakrishnan_2015,
doi = {10.1063/1.4928757},
url = {https://doi.org/10.1063%2F1.4928757},
year = 2015,
month = {aug},
publisher = {{AIP} Publishing},
volume = {143},
number = {8},
author = {Raghunathan Ramakrishnan and Mia Hartmann
and Enrico Tapavicza and O. Anatole von Lilienfeld},
title = {Electronic spectra from {TDDFT}
and machine learning in chemical space},
journal = {The Journal of Chemical Physics}
}
@article{Wu_2018,
doi = {10.1039/c7sc02664a},
url = {https://doi.org/10.1039%2Fc7sc02664a},
year = 2018,
publisher = {Royal Society of Chemistry ({RSC})},
volume = {9},
number = {2},
pages = {513--530},
author = {Zhenqin Wu and Bharath Ramsundar and Evan~N. Feinberg
and Joseph Gomes and Caleb Geniesse and Aneesh S. Pappu
and Karl Leswing and Vijay Pande},
title = {{MoleculeNet}: a benchmark for molecular machine learning},
journal = {Chem. Sci.}
}
```

## 数据集详情

### 数据集描述
QM8是针对小分子电子光谱与激发态能级的量子力学模拟数据集。该数据集针对一组至多包含8个重原子的分子集合，应用了含时密度泛函理论（time-dependent density functional theories, TDDFT）与二阶近似耦合簇（second-order approximate coupled-cluster, CC2）等多种计算技术。这些分子同时也是GDB-17数据库的子集。

- **整理方：**
- **许可协议：** CC BY 4.0

### 数据集来源
- [原始数据集](https://deepchemdata.s3-us-west-1.amazonaws.com/datasets/gdb8.tar.gz)

## 引用

**BibTeX：**

bibtex
@article{Blum_2009,
doi = {10.1021/ja902302h},
url = {https://doi.org/10.1021%2Fja902302h},
year = 2009,
month = {jun},
publisher = {美国化学会（ACS）},
volume = {131},
number = {25},
pages = {8732--8733},
author = {Lorenz C. Blum、Jean-Louis Reymond},
title = {化学宇宙数据库GDB-13中用于虚拟筛选的9.7亿个类药小分子},
journal = {J. Am. Chem. Soc.}
}
@article{Ramakrishnan_2015,
doi = {10.1063/1.4928757},
url = {https://doi.org/10.1063%2F1.4928757},
year = 2015,
month = {aug},
publisher = {AIP出版集团},
volume = {143},
number = {8},
author = {Raghunathan Ramakrishnan、Mia Hartmann、Enrico Tapavicza、O. Anatole von Lilienfeld},
title = {化学空间中基于TDDFT与机器学习的电子光谱},
journal = {The Journal of Chemical Physics}
}
@article{Wu_2018,
doi = {10.1039/c7sc02664a},
url = {https://doi.org/10.1039%2Fc7sc02664a},
year = 2018,
publisher = {英国皇家化学会（RSC）},
volume = {9},
number = {2},
pages = {513--530},
author = {Zhenqin Wu、Bharath Ramsundar、Evan~N. Feinberg、Joseph Gomes、Caleb Geniesse、Aneesh S. Pappu、Karl Leswing、Vijay Pande},
title = {{MoleculeNet}：分子机器学习基准数据集},
journal = {Chem. Sci.}
}