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.
Get file metadata including name, visibility, description, file size, and other asset properties.
import os
from 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 = "39fa764a-4fc8-4dad-bc98-0bb1cc696ca9"
# Retrieve file metadata
file = ouro.files.retrieve(file_id)
print(file.name, file.visibility)
print(file.metadata)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 file
file_data = file.read_data()
print(file_data.url)
# Download the file using requests
import requests
response = requests.get(file_data.url)
with open('downloaded_file', 'wb') as output_file:
output_file.write(response.content)Update file metadata (name, description, visibility, etc.) and optionally replace the file data with a new file. Requires write or admin permission.
# Update file metadata
updated = ouro.files.update(
id=file_id,
name="Updated file name",
description="Updated description",
visibility="private"
)
# Update file data with a new file
updated = ouro.files.update(
id=file_id,
file_path="./new_file.txt"
)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)Increased demand for high-performance permanent magnets in the electric vehicle and wind turbine industries has prompted the search for cost-effective alternatives. Nevertheless, the discovery of new magnetic materials with the desired intrinsic and extrinsic permanent magnet properties presents a significant challenge. Traditional Density Functional Theory (DFT) accurately predicts intrinsic permanent magnet properties such as magnetic moments, magneto-crystalline anisotropy constants, and exchange interactions. However, it cannot compute extrinsic macroscopic properties, such as coercivity (Hc), which are influenced by factors like microscopic defects and internal grain structures. Although micromagnetic simulation helps compute Hc, it overestimates the values almost by an order of magnitude due to Brown’s paradox. To circumvent these limitations, we employ Machine Learning (ML) methods in an extensive database obtained from experiments, DFT calculations, and micromagnetic modeling. Our novel ML approach is computationally much faster than the micromagnetic simulation program, the mumax3. We successfully utilize it to predict Hc values for materials like cerium-doped Nd2Fe14B, and subsequently compare the predicted values with experimental results. Remarkably, our ML model accurately identifies uniaxial magnetic anisotropy as the primary contributor to Hc. With DFT calculations, we predict the Nd-site dependent magnetic anisotropy behavior in Nd2Fe14B, confirming 4f-site planar and 4g-site uniaxial to crystalline c-direction in good agreement with experiment. The Green’s function atomic sphere approximation calculated a Curie temperature (TC) for Nd2Fe14B that also agrees well with experiment.
Paper by Churna Bhandari, Gavin N. Nop, Jonathan D.H. Smith, Durga Paudyal