Open-Catalyst-Dataset

THIS REPOSITORY HAS BEEN MOVED TO http://github.com/fair-chem/fairchem/tree/main/src/fairchem/data/oc

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Open-Catalyst-Dataset

This repository hosts the adsorbate-catalyst input generation workflow used in the Open Catalyst Project.

Setup

The easiest way to install prerequisites is via conda. After installing conda, run the following commands:

Workflow

The codebase supports the following workflow to generate adsorbate-catalyst input configurations.

  1. Initialize a bulk:
    • By providing an atoms object, or
    • By bulk_id (e.g. mp-30), or
    • By its index in the database, or
    • By selecting randomly.
  2. Initialize an adsorbate:
    • By providing an atoms object, or
    • By its SMILES string (e.g. *H), or
    • By its index in the database, or
    • By selecting randomly.
  3. Enumerate slabs from the Bulk class.
    This internally uses pymatgen.core.surface.SlabGenerator and supports the following:
    • All slabs up to a specified miller index, or
    • A random slab among those enumerated by the previous method, or
    • A specific miller index.
  4. Place the adsorbate on the slab.
    This broadly has two steps – identifying a binding site on the surface of the slab, and orienting the adsorbate before placing it at that site. We use custom code inspired by pymatgen to do this. There are 3 modes: heuristic, random, and random_site_heuristic_placement.
    • Identifying a binding site: First, a Delaunay meshgrid is constructed with surface atoms as nodes. For heuristic, the sites considered are on the node (atop), between 2 nodes (bridge) and in the center of the triangle (hollow). For random and random_site_heuristic_placement, positions of the sites are uniformly randomly sampled along the Delaunay triangles.
    • Adsorbate orientation: For heuristic and random_site_heuristic_placement, the adsorbate is uniformly randomly rotated around the z direction, and provided a slight wobble around x and y, which amounts to randomized tilt within a certain cone around the north pole. For random, the adsorbate is uniformly randomly rotated about its center of mass along all directions.
    • Binding atom: The adsorbate database includes information about which atoms are expected to bind. For heuristic and random_site_heuristic_placement, the binding atom of the adsorbate is placed at the site, whereas for random the center of mass of the adsorbate is placed at the site.

Workflow image

Usage

Here is a simple example using the ocdata workflow to place CO on Cu (1,1,1):

bulk_src_id = "mp-30"
adsorbate_smiles = "*CO"

bulk = Bulk(bulk_src_id_from_db=bulk_src_id, bulk_db_path="your-path-here.pkl")
adsorbate = Adsorbate(adsorbate_smiles_from_db=adsorbate_smiles, adsorbate_db_path="your-path-here.pkl")
slabs = Slab.from_bulk_get_specific_millers(bulk=bulk, specific_millers=(1,1,1))

# Perform heuristic placements
heuristic_adslabs = AdsorbateSlabConfig(slabs[0], adsorbate, mode="heuristic")

# Perform random site, heuristic placements
random_adslabs = AdsorbateSlabConfig(slabs[0], adsorbate, mode="random_site_heuristic_placement", num_sites=100)

If you want to use a bulk and/or adsorbate that is not in the database here, you may supply your own ase.Atoms object:

bulk = Bulk(bulk_atoms=your_adsorbate_atoms)
adsorbate = Adsorbate(adsorbate_atoms=your_adsorbate_atoms)
slabs = Slab.from_bulk_get_all_slabs(bulk)

# Perform fully random placements
random_adslabs = AdsorbateSlabConfig(slabs[0], adsorbate, mode="random", num_sites=100)

If you would like to randomly choose a bulk, adsorbate, and slab:

bulk = Bulk()
adsorbate = Adsorbate()
slab = Slab.from_bulk_get_random_slab(bulk)

# Perform fully random placements
random_adslabs = AdsorbateSlabConfig(slab, adsorbate, mode="random", num_sites=100)

StructureGenerator API

We also provide a StructureGenerator helper class that wraps the core functionality described above for creating bulk/slab/adsorbate objects, and writing vasp input files and metadata for multiple placements of the adsorbate on the slab. There are a number of options to configure input generation to suit different usecases. We list a few examples here.

Command Line Args

Input files:

Bulk / Slab / Adsorbate specification

Option 1: provide indices. All three must be provided to generate adsorbate-slab configurations, otherwise only slab enumeration will be performed.

Option 2: provide a set of indices (one of the following)

Slab enumeration

Adsorbate Placement

Multiprocessing, when given a file of indices

Outputs

Usage

python structure_generator.py \
  --bulk_db databases/pkls/bulks.pkl \
  --adsorbate_db databases/pkls/adsorbates.pkl  \
  --output_dir outputs/ \
  --adsorbate_index 0 \
  --bulk_index 0 \
  --surface_index 0 \
  --heuristic_placements

python structure_generator.py \
  --bulk_db databases/pkls/bulks.pkl \
  --adsorbate_db databases/pkls/adsorbates.pkl  \
  --indices_file your_index_file.txt \
  --seed 0 \
  --random_placements \
  --random_sites 100

Databases for bulks and adsorbates

Bulks

A database of bulk materials taken from existing databases (i.e. Materials Project) and relaxed with consistent RPBE settings may be found in ocdata/databases/pkls/bulks.pkl. To preview what bulks are available, view the corresponding mapping between indices and bulks (bulk id and composition): https://dl.fbaipublicfiles.com/opencatalystproject/data/input_generation/mapping_bulks_2021sep20.txt

Adsorbates

A database of adsorbates may be found in ocdata/databases/pkls/adsorbates.pkl. Alternatively, it may be downloaded using the following link: The latest version is https://dl.fbaipublicfiles.com/opencatalystproject/data/input_generation/adsorbate_db_2021apr28.pkl (MD5 checksum: 975e00a62c7b634b245102e42167b3fb). To preview what adsorbates are available, view the corresponding mapping between indices and adsorbates (SMILES): https://dl.fbaipublicfiles.com/opencatalystproject/data/input_generation/mapping_adsorbates_2020may12.txt

Previous snapshots of the codebase

License

ocdata is released under the MIT license.

Citation

If you use this codebase in your work, please consider citing:

@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},
}

The Open Catalyst 2020 (OC20) and Open Catalyst 2022 (OC22) datasets are licensed under a Creative Commons Attribution 4.0 License.

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