Learn how to interact with this file using the Ouro SDK or REST API.
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 = "c9e71a9d-4ccb-49a0-8ea8-c4d7a2dd0837"# 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 hull
file.cif→file.html
9mo
2.4k uses
Predict the Curie temperature of a material
file.cif→JSON
1y
2.3k uses
Calculate the estimated raw material cost per kg
file.cif→JSON
9mo
1.6k uses
Relax a crystal structure
file.cif→file.cif
9mo
1.5k uses
Calculate magnetic saturation and related properties
file.cif→JSON
10mo
767 uses
Calculate phonon dispersion and return band structure plot
file.cif→file.png
9mo
564 uses
Create a supercell from a material
file.cif→file.cif
11mo
123 uses
Get basic structural information from a CIF file
file.cif→JSON
10mo
119 uses
Estimate ZT and key thermoelectric properties
file.cif→JSON
20d
76 uses
Relax a crystal structure with animation
file.cif→file.mp4
8mo
22 uses
Create interstitially doped structure
file.cif→file.cif
8mo
21 uses
Structure relaxation via NequIP-OAM-XL
file.cif→file.cif
20d
19 uses
Relax a crystal structure and create a post
file.cif→post
8mo
14 uses
Get a detailed description of a crystal structure
file.cif→JSON
8mo
12 uses
Predict formation energy per atom (MP dataset)
file.cif→JSON
15d
11 uses
Predict total magnetic moment per cell
file.cif→JSON
15d
10 uses
Predict Seebeck coefficient and band gap
file.cif→JSON
20d
10 uses
Calculate magnetic anisotropy energy
file.cif→JSON
4mo
6 uses
Predict energy above the convex hull
file.cif→JSON
15d
6 uses
Synthesis report from CIF file
file.cif→file.html
2mo
5 uses
Predict superconducting critical temperature
file.cif→JSON
15d
4 uses
Check phonon stability
file.cif→file.png
20d
2 uses
Predict band gap using the TBmBJ functional
file.cif→JSON
15d
2 uses
Predict static dielectric function (εx)
file.cif→JSON
15d
1 use
Predict average electron effective mass
file.cif→JSON
15d
1 use
Predict electronic dielectric function (ε∞x)
file.cif→JSON
15d
Predict maximum dielectric constant from DFPT
file.cif→JSON
15d
Predict maximum piezoelectric strain coefficient dij
file.cif→JSON
15d
Predict Voigt bulk modulus
file.cif→JSON
15d
Predict Voigt shear modulus
file.cif→JSON
15d
Predict exfoliation energy for layered materials
file.cif→JSON
15d
Predict n-type Seebeck coefficient
file.cif→JSON
15d
Predict p-type Seebeck coefficient
file.cif→JSON
15d
Predict n-type thermoelectric power factor
file.cif→JSON
15d
Predict maximum electric field gradient
file.cif→JSON
15d
Predict electronic DOS at Fermi level
file.cif→JSON
15d
Predict Debye temperature for superconductor analysis
file.cif→JSON
15d
Predict Eliashberg spectral function α²F(ω)
file.cif→JSON
15d
Predict phonon density of states
file.cif→JSON
15d
Predict optimal k-point length for DFT convergence
AI-discovered magnetic material: Mn2CrFe4Co4N (performance score: 0.740) | Space group: 1 (resolved from structure) | Key properties: Tc: 612K, Ms: 0.14T, Cost: $13/kg, E_hull: 0.235eV/atom, Dynamically stable | Discovered in 20 AI iterations | - The combination of Mn, Cr, Fe, Co, and N in this stoichiometry yields a high Curie temperature and magnetic density.
The material is dynamically stable, which supports its structural integrity.
The energy above hull suggests that the material is metastable or unstable thermodynamically.
Cost is low, indicating practical feasibility from an economic standpoint.