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 = "de4fd0e3-713c-4d3c-84b1-6781bbea4db1"# 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
2.5k uses
Predict the Curie temperature of a material
file.cif→JSON
2.3k uses
Relax a crystal structure
file.cif→file.cif
1.8k uses
Calculate the estimated raw material cost per kg
file.cif→JSON
1.6k uses
Calculate magnetic saturation and related properties
file.cif→JSON
799 uses
Calculate phonon dispersion and band structure
file.cif→file.png
568 uses
Calculate magnetic anisotropy energy (MAE)
file.cif→JSON
142 uses
Create a supercell from a material
file.cif→file.cif
135 uses
Get basic structural information from a CIF file
file.cif→JSON
122 uses
Estimate ZT and key thermoelectric properties
file.cif→JSON
76 uses
Structure relaxation via NequIP-OAM-XL
file.cif→file.cif
31 uses
Relax a crystal structure with animation
file.cif→file.mp4
27 uses
Relax a crystal structure and publish results
file.cif→post
22 uses
Create an interstitially doped structure
file.cif→file.cif
21 uses
Predict total magnetic moment per cell
file.cif→JSON
20 uses
Predict energy above the convex hull
file.cif→JSON
20 uses
Predict superconducting critical temperature
file.cif→JSON
17 uses
Predict formation energy per atom (MP dataset)
file.cif→JSON
16 uses
Calculate magnetic anisotropy energy
file.cif→JSON
14 uses
Predict Seebeck coefficient and band gap
file.cif→JSON
10 uses
Predict formation energy per atom (optB88vdW)
file.cif→JSON
8 uses
Simulate an X-ray diffraction pattern
file.cif→file.xy
6 uses
Synthesis report from CIF file
file.cif→file.html
5 uses
Check phonon stability
file.cif→file.png
3 uses
Predict static dielectric function (εx)
file.cif→JSON
2 uses
Predict band gap using the TBmBJ functional
file.cif→JSON
2 uses
Predict total energy per atom (optB88vdW)
file.cif→JSON
2 uses
Predict average electron effective mass
file.cif→JSON
1 use
Predict electronic dielectric function (ε∞x)
file.cif→JSON
Predict maximum dielectric constant from DFPT
file.cif→JSON
Predict maximum piezoelectric strain coefficient dij
file.cif→JSON
Predict Voigt bulk modulus
file.cif→JSON
Predict Voigt shear modulus
file.cif→JSON
Predict exfoliation energy for layered materials
file.cif→JSON
Predict n-type Seebeck coefficient
file.cif→JSON
Predict p-type Seebeck coefficient
file.cif→JSON
Predict n-type thermoelectric power factor
file.cif→JSON
Predict maximum electric field gradient
file.cif→JSON
Predict electronic DOS at Fermi level
file.cif→JSON
Predict Debye temperature for superconductor analysis
file.cif→JSON
Predict Eliashberg spectral function α²F(ω)
file.cif→JSON
Predict phonon density of states
file.cif→JSON
Predict optimal k-point length for DFT convergence
AI-discovered magnetic material: MnFe4(CoB2)2 (performance score: 0.731) | Space group: 38 (resolved from structure) | Key properties: Tc: 518K, Ms: 0.12T, Cost: $10/kg, E_hull: 0.164eV/atom, Dynamically stable | Discovered in 2 AI iterations | The material MnFe4(CoB2)2 demonstrates promising magnetic properties with a Curie temperature above 500 K and magnetic density above 0.1, confirming its potential as a high-performance magnetic material. Its low cost and dynamic stability are additional advantages. The slight excess in energy above hull indicates that minor compositional or structural tuning might be needed to improve thermodynamic stability. This suggests that the compound is close to being stable and could be optimized further.