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README.md
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---
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license: apache-2.0
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dataset_info:
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features:
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- name: input_ids
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sequence: int16
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- name: coords
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sequence:
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sequence: float32
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- name: forces
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sequence:
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sequence: float32
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- name: formation_energy
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dtype: float32
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- name: total_energy
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dtype: float32
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- name: has_formation_energy
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dtype: bool
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- name: length
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dtype: int64
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splits:
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- name: train
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num_bytes: 43353603080
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num_examples: 15000000
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download_size: 44763791790
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dataset_size: 43353603080
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configs:
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- config_name: default
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data_files:
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- split: train
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path: data/train-*
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---
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---
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license: apache-2.0
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dataset_info:
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features:
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- name: input_ids
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sequence: int16
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- name: coords
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sequence:
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sequence: float32
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- name: forces
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sequence:
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sequence: float32
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- name: formation_energy
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dtype: float32
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- name: total_energy
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dtype: float32
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- name: has_formation_energy
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dtype: bool
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- name: length
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dtype: int64
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splits:
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- name: train
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num_bytes: 43353603080
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num_examples: 15000000
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download_size: 44763791790
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dataset_size: 43353603080
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configs:
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- config_name: default
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data_files:
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- split: train
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path: data/train-*
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---
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## Dataset Description
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This dataset contains a collection of 3D atomistic datasets with force and energy labels gathered from a series of sources:
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- [Open Catalyst Project](https://github.com/FAIR-Chem/fairchem)
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- OC20, OC22, ODAC23
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- [Materials Project Trajectory Dataset (MPtrj)](https://figshare.com/articles/dataset/Materials_Project_Trjectory_MPtrj_Dataset/23713842)
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- [SPICE 1.1.4](https://www.nature.com/articles/s41597-022-01882-6)
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## Dataset Structure
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### Data Instances
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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
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system (`total_energy`/`formation_energy`) and a boolean `has_formation_energy` that signifies whether the dataset has a valid formation energy.
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```
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{'input_ids': [26, 28, 28, 28],
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'coords': [[0.0, 0.0, 0.0],
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[0.0, 0.0, 3.5395920276641846],
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[0.0, 1.7669789791107178, 1.7697960138320923],
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[1.7669789791107178, 0.0, 1.7697960138320923]],
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'forces': [[-1.999999987845058e-08, 2.999999892949745e-08, -0.0],
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[-5.99999978589949e-08, 5.99999978589949e-08, 9.99999993922529e-09],
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[-0.0014535699738189578, 0.0014535400550812483, 9.99999993922529e-09],
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[0.001453649951145053, -0.0014536300441250205, -2.999999892949745e-08]],
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'formation_energy': 0.6030612587928772,
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'total_energy': -25.20570182800293,
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'has_formation_energy': True}
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```
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The numbers of atoms within each sample for each dataset varies but the number of samples for each dataset is balanced.
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`MPtrj` and `SPICE` are upsampled 2x and 3x respectively to ensure a balanced dataset distribution. The datasets are
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interleaved until we run out of samples where there are 3,160,790 systems from each dataset (2x MPtrj runs out of samples first).
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### Citation Information
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```
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@article{ocp_dataset,
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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},
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title = {Open Catalyst 2020 (OC20) Dataset and Community Challenges},
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journal = {ACS Catalysis},
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year = {2021},
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doi = {10.1021/acscatal.0c04525},
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}
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```
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```
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@article{oc22_dataset,
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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},
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title = {The Open Catalyst 2022 (OC22) dataset and challenges for oxide electrocatalysts},
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journal = {ACS Catalysis},
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year={2023},
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}
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```
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```
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@article{odac23_dataset,
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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},
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title = {The Open DAC 2023 Dataset and Challenges for Sorbent Discovery in Direct Air Capture},
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year = {2023},
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journal={arXiv preprint arXiv:2311.00341},
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}
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```
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```
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@article{deng_2023_chgnet,
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author={Deng, Bowen and Zhong, Peichen and Jun, KyuJung and Riebesell, Janosh and Han, Kevin and Bartel, Christopher J. and Ceder, Gerbrand},
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title={CHGNet as a pretrained universal neural network potential for charge-informed atomistic modelling},
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journal={Nature Machine Intelligence},
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year={2023},
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DOI={10.1038/s42256-023-00716-3},
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pages={1–11}
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}
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```
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```
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@article{eastman2023spice,
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title={Spice, a dataset of drug-like molecules and peptides for training machine learning potentials},
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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},
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journal={Scientific Data},
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volume={10},
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number={1},
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pages={11},
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year={2023},
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publisher={Nature Publishing Group UK London}
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}
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```
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