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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 = "6362e1ed-e846-4c1c-9140-f817169d03aa"# 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
2.5k 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
10mo
1.6k uses
Calculate magnetic saturation and related properties
file.cif→JSON
10mo
768 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
127 uses
Get basic structural information from a CIF file
file.cif→JSON
10mo
119 uses
Estimate ZT and key thermoelectric properties
file.cif→JSON
23d
76 uses
Structure relaxation via NequIP-OAM-XL
file.cif→file.cif
23d
25 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
Predict energy above the convex hull
file.cif→JSON
19d
15 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
19d
11 uses
Predict total magnetic moment per cell
file.cif→JSON
19d
10 uses
Predict Seebeck coefficient and band gap
file.cif→JSON
23d
10 uses
Calculate magnetic anisotropy energy
file.cif→JSON
4mo
6 uses
Synthesis report from CIF file
file.cif→file.html
2mo
5 uses
Predict superconducting critical temperature
file.cif→JSON
19d
4 uses
Check phonon stability
file.cif→file.png
23d
2 uses
Predict band gap using the TBmBJ functional
file.cif→JSON
19d
2 uses
Predict static dielectric function (εx)
file.cif→JSON
19d
1 use
Predict average electron effective mass
file.cif→JSON
19d
1 use
Predict electronic dielectric function (ε∞x)
file.cif→JSON
19d
Predict maximum dielectric constant from DFPT
file.cif→JSON
19d
Predict maximum piezoelectric strain coefficient dij
file.cif→JSON
19d
Predict Voigt bulk modulus
file.cif→JSON
19d
Predict Voigt shear modulus
file.cif→JSON
19d
Predict exfoliation energy for layered materials
file.cif→JSON
19d
Predict n-type Seebeck coefficient
file.cif→JSON
19d
Predict p-type Seebeck coefficient
file.cif→JSON
19d
Predict n-type thermoelectric power factor
file.cif→JSON
19d
Predict maximum electric field gradient
file.cif→JSON
19d
Predict electronic DOS at Fermi level
file.cif→JSON
19d
Predict Debye temperature for superconductor analysis
file.cif→JSON
19d
Predict Eliashberg spectral function α²F(ω)
file.cif→JSON
19d
Predict phonon density of states
file.cif→JSON
19d
Predict optimal k-point length for DFT convergence
Crystal structure for Fe2CoMnW | Space group: 156 (resolved from structure) | Generated from scratch using crystal structure prediction | Number of atoms: 5 | Generated: 2025-12-15 14:22:31
AI-discovered magnetic material: Fe2CoMnW (performance score: 0.810) | Space group: 156 (resolved from structure) | AI-generated from scratch using crystal structure prediction | Key properties: Tc: 555K, Ms: 0.11T, MAE: 5.50mJ/m^3, Cost: $21/kg, E_hull: 0.262eV/atom, Dynamically stable | Discovered in 3 AI iterations | This material demonstrates that high magnetic performance can be achieved with relatively low cost and a small unit cell size. The high Curie temperature and magnetic anisotropy energy suggest potential for magnetic applications requiring thermal stability and strong anisotropy. The dynamic stability is a positive sign for synthesis feasibility. However, the elevated energy above hull suggests that further optimization or doping might be needed to improve thermodynamic stability. This insight highlights a trade-off between achieving strong magnetic properties and maintaining low energy above hull in this chemical composition and structure.