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API access requires an API key. Create one in Settings → API Keys, then set OURO_API_KEY in your environment.
Retrieve file
Get file metadata including name, visibility, description, file size, and other asset properties.
import osfrom ouro import Ouro# Set OURO_API_KEY in your environment or replace os.environ.get("OURO_API_KEY")ouro = Ouro(api_key=os.environ.get("OURO_API_KEY"))file_id = "c8510c50-ad93-4478-a0a2-455007fef83f"# Retrieve file metadatafile = ouro.files.retrieve(file_id)print(file.name, file.visibility)print(file.metadata)
Read file data
Get a URL to download or embed the file. For private assets, the URL is temporary and will expire after 1 hour.
# Get signed URL to download the filefile_data = file.read_data()print(file_data.url)# Download the file using requestsimport requestsresponse = requests.get(file_data.url)with open('downloaded_file', 'wb') as output_file: output_file.write(response.content)
Update file
Update file metadata (name, description, visibility, etc.) and optionally replace the file data with a new file. Requires write or admin permission.
# Update file metadataupdated = ouro.files.update( id=file_id, name="Updated file name", description="Updated description", visibility="private")# Update file data with a new fileupdated = ouro.files.update( id=file_id, file_path="./new_file.txt")
Delete file
Permanently delete a file from the platform. Requires admin permission. This action cannot be undone.
# Delete a file (requires admin permission)ouro.files.delete(id=file_id)
Calculate energy above the convex hull
file.cif→file.html
10mo
2.5k uses
Predict the Curie temperature of a material
file.cif→JSON
1y
2.3k uses
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file.cif→JSON
9mo
1.6k uses
Relax a crystal structure
file.cif→file.cif
10mo
1.6k uses
Calculate magnetic saturation and related properties
file.cif→JSON
10mo
776 uses
Calculate phonon dispersion and band structure
file.cif→file.png
10mo
564 uses
Create a supercell from a material
file.cif→file.cif
1y
128 uses
Get basic structural information from a CIF file
file.cif→JSON
10mo
119 uses
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file.cif→JSON
1mo
76 uses
Structure relaxation via NequIP-OAM-XL
file.cif→file.cif
1mo
31 uses
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file.cif→file.mp4
9mo
25 uses
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file.cif→file.cif
9mo
21 uses
Predict energy above the convex hull
file.cif→JSON
1mo
17 uses
Predict total magnetic moment per cell
file.cif→JSON
1mo
16 uses
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file.cif→post
9mo
14 uses
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file.cif→JSON
9mo
12 uses
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file.cif→JSON
1mo
11 uses
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file.cif→JSON
1mo
10 uses
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file.cif→JSON
4mo
8 uses
Synthesis report from CIF file
file.cif→file.html
3mo
5 uses
Predict superconducting critical temperature
file.cif→JSON
1mo
4 uses
Check phonon stability
file.cif→file.png
1mo
2 uses
Predict band gap using the TBmBJ functional
file.cif→JSON
1mo
2 uses
Predict static dielectric function (εx)
file.cif→JSON
1mo
1 use
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file.cif→JSON
1mo
1 use
Simulate an X-ray diffraction pattern
file.cif→file.html
18d
1 use
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file.cif→JSON
1mo
Predict maximum dielectric constant from DFPT
file.cif→JSON
1mo
Predict maximum piezoelectric strain coefficient dij
file.cif→JSON
1mo
Predict Voigt bulk modulus
file.cif→JSON
1mo
Predict Voigt shear modulus
file.cif→JSON
1mo
Predict exfoliation energy for layered materials
file.cif→JSON
1mo
Predict n-type Seebeck coefficient
file.cif→JSON
1mo
Predict p-type Seebeck coefficient
file.cif→JSON
1mo
Predict n-type thermoelectric power factor
file.cif→JSON
1mo
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file.cif→JSON
1mo
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file.cif→JSON
1mo
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file.cif→JSON
1mo
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file.cif→JSON
1mo
Predict phonon density of states
file.cif→JSON
1mo
Predict optimal k-point length for DFT convergence
Experiments with diffusion models to generate crystal structures, moving from noisy representations to concrete atomic arrangements. They describe learning how these models can learn structure without strict physical rules, and compare approaches that rely on fixed constraints to ones that let the model discover valid layouts. The author notes limitations in existing crystal generators, such as only producing tiny unit cells and struggling with complex, multi-atom systems like NdFeB. To address this, they explore modeling larger supercells with hundreds of atoms to improve detail and tolerance to errors, potentially revealing new properties through dopants. They keep a running experiment log in an AI notebook and plan to explore conditioning methods and the difference between flow and diffusion approaches in future work.