README.md

---
license: apache-2.0
dataset_info:
  features:
  - name: input_ids
    sequence: int16
  - name: coords
    sequence:
      sequence: float32
  - name: forces
    sequence:
      sequence: float32
  - name: formation_energy
    dtype: float32
  - name: total_energy
    dtype: float32
  - name: has_formation_energy
    dtype: bool
  - name: length
    dtype: int64
  splits:
  - name: train
    num_bytes: 43353603080
    num_examples: 15000000
  download_size: 44763791790
  dataset_size: 43353603080
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
---

## Dataset Description

This dataset contains a collection of 3D atomistic datasets with force and energy labels gathered from a series of sources:

- [Open Catalyst Project](https://github.com/FAIR-Chem/fairchem)
  - OC20, OC22, ODAC23
- [Materials Project Trajectory Dataset (MPtrj)](https://figshare.com/articles/dataset/Materials_Project_Trjectory_MPtrj_Dataset/23713842)
- [SPICE 1.1.4](https://www.nature.com/articles/s41597-022-01882-6)

## Dataset Structure

### Data Instances

For each instance, there is set of atomic numbers (`input_ids`), 3-D coordinates (`coords`), a set of forces per atom  (`forces`), the total and formation energy per
system (`total_energy`/`formation_energy`) and a boolean `has_formation_energy` that signifies whether the dataset has a valid formation energy.

```
{'input_ids': [26, 28, 28, 28],
 'coords': [[0.0, 0.0, 0.0],
  [0.0, 0.0, 3.5395920276641846],
  [0.0, 1.7669789791107178, 1.7697960138320923],
  [1.7669789791107178, 0.0, 1.7697960138320923]],
 'forces': [[-1.999999987845058e-08, 2.999999892949745e-08, -0.0],
  [-5.99999978589949e-08, 5.99999978589949e-08, 9.99999993922529e-09],
  [-0.0014535699738189578, 0.0014535400550812483, 9.99999993922529e-09],
  [0.001453649951145053, -0.0014536300441250205, -2.999999892949745e-08]],
 'formation_energy': 0.6030612587928772,
 'total_energy': -25.20570182800293,
 'has_formation_energy': True}
```

The numbers of atoms within each sample for each dataset varies but the number of samples for each dataset is balanced.  
`MPtrj` and `SPICE` are upsampled 2x and 3x respectively to ensure a balanced dataset distribution.  The datasets are 
interleaved until we run out of samples where there are 3,160,790 systems from each dataset (2x MPtrj runs out of samples first).


### Citation Information

```
@article{ocp_dataset,
    author = {Chanussot*, Lowik and Das*, Abhishek and Goyal*, Siddharth and Lavril*, Thibaut and Shuaibi*, Muhammed and Riviere, Morgane and Tran, Kevin and Heras-Domingo, Javier and Ho, Caleb and Hu, Weihua and Palizhati, Aini and Sriram, Anuroop and Wood, Brandon and Yoon, Junwoong and Parikh, Devi and Zitnick, C. Lawrence and Ulissi, Zachary},
    title = {Open Catalyst 2020 (OC20) Dataset and Community Challenges},
    journal = {ACS Catalysis},
    year = {2021},
    doi = {10.1021/acscatal.0c04525},
}
```

```
@article{oc22_dataset,
    author = {Tran*, Richard and Lan*, Janice and Shuaibi*, Muhammed and Wood*, Brandon and Goyal*, Siddharth and Das, Abhishek and Heras-Domingo, Javier and Kolluru, Adeesh and Rizvi, Ammar and Shoghi, Nima and Sriram, Anuroop and Ulissi, Zachary and Zitnick, C. Lawrence},
    title = {The Open Catalyst 2022 (OC22) dataset and challenges for oxide electrocatalysts},
    journal = {ACS Catalysis},
    year={2023},
}
```

```
@article{odac23_dataset,
    author = {Anuroop Sriram and Sihoon Choi and Xiaohan Yu and Logan M. Brabson and Abhishek Das and Zachary Ulissi and Matt Uyttendaele and Andrew J. Medford and David S. Sholl},
    title = {The Open DAC 2023 Dataset and Challenges for Sorbent Discovery in Direct Air Capture},
    year = {2023},
    journal={arXiv preprint arXiv:2311.00341},
}
```

```
@article{deng_2023_chgnet,
    author={Deng, Bowen and Zhong, Peichen and Jun, KyuJung and Riebesell, Janosh and Han, Kevin and Bartel, Christopher J. and Ceder, Gerbrand},
    title={CHGNet as a pretrained universal neural network potential for charge-informed atomistic modelling},
    journal={Nature Machine Intelligence},
    year={2023},
    DOI={10.1038/s42256-023-00716-3},
    pages={1–11}
}
```

```
@article{eastman2023spice,
  title={Spice, a dataset of drug-like molecules and peptides for training machine learning potentials},
  author={Eastman, Peter and Behara, Pavan Kumar and Dotson, David L and Galvelis, Raimondas and Herr, John E and Horton, Josh T and Mao, Yuezhi and Chodera, John D and Pritchard, Benjamin P and Wang, Yuanqing and others},
  journal={Scientific Data},
  volume={10},
  number={1},
  pages={11},
  year={2023},
  publisher={Nature Publishing Group UK London}
}
```