Python SDK requires an API key. Create one in Settings → API Keys, then set OURO_API_KEY.
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 = "5bd21f92-d801-4fe4-a1fd-e62a5ee4140c"
# Retrieve file metadata and signed URL
f = ouro.files.retrieve(file_id)
print(f.name, f.visibility)
data = f.read_data() # fetches signed URL
print(data.url)Machine-Learning Prediction of Curie Temperature from Chemical Compositions of Ferromagnetic Materials
Room-temperature ferromagnets are high-value targets for discovery given the ease by which they could be embedded within magnetic devices. However, the multitude of potential interactions among magnetic ions and their surrounding environments renders the prediction of thermally stable magnetic properties challenging. Therefore, it is vital to explore methods that can effectively screen potential candidates to expedite the discovery of novel ferromagnetic materials within highly intricate feature spaces. To this end, the authors explore machine-learning (ML) methods as a means to predict the Curie temperature (Tc) of ferromagnetic materials by discerning patterns within materials databases.
Notes from ML prediction of Curie temperature paper
postSharing some notes as I read this paper. I uploaded it here for reference. I came across it looking for a Curie temperature dataset and so far this has been the best I've found so far.
7mo