10mo
Python SDK requires an API key. Create one in Settings → API Keys, then set OURO_API_KEY.
python
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 = "e915c1d2-5203-4a29-8d01-019ad050df06"
# 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)Evaluating on target out-of-distribution samples
Cutting the dataset to samples with Tc below 80 K, we find that the model is unable to make any predictions greater than 80 K. Not very surprising, but a critical failure for what we need this model to do.
After reading the MatterSim paper, the authors proposed the idea of using the MLFF's latent space as a direct property prediction feature set. Earlier, and I had been thinking about using a VAE (or s
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