Here, we report a universal IAP for materials based on graph neural networks with three-body interactions (M3GNet). The M3GNet IAP was trained on the massive database of structural relaxations performed by the Materials Project over the past 10 years and has broad applications in structural relaxation, dynamic simulations and property prediction of materials across diverse chemical spaces.
Chi Chen & Shyue Ping Ong
https://www.nature.com/articles/s43588-022-00349-3
Preprint version from arXiv
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# Delete a file (requires admin permission)
ouro.files.delete(id=file_id)# 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
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"
)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 = "31bc8f4d-0e5a-45d3-94de-5f5c11fef14d"
# Retrieve file metadata
file = ouro.files.retrieve(file_id)
print(file.name, file.visibility)
print(file.metadata)