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 = "c822cb5a-50c5-43c3-b8a0-df9e00b72385"
# 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 modeling of superconducting critical temperature
Superconductivity has been the focus of enormous research effort since its discovery more than a century ago. Yet, some features of this unique phenomenon remain poorly understood; prime among these is the connection between superconductivity and chemical/structural properties of materials. To bridge the gap, several machine learning schemes are developed herein to model the critical temperatures (Tc) of the 12,000+ known superconductors available via the SuperCon database. Materials are first divided into two classes based on their Tc values, above and below 10 K, and a classification model predicting this label is trained. The model uses coarse-grained features based only on the chemical compositions. It shows strong predictive power, with out-of-sample accuracy of about 92%.
https://www.nature.com/articles/s41524-018-0085-8
So far a really interesting paper. Published in 2018. Adding some informal notes and interesting findings here. Finding out how much literature is based on this study.
10mo