Predict total magnetic moment per unit cell (μB) and Ms / μ₀ Ms from a CIF. Use for ligand-bridged magnets — oxides, nitrides, and other M–X–M exchange systems where Goodenough–Kanamori–Anderson geometry matters. Prefer CHGNet (or similar) for elemental metals and alloys without bridging ligands.
Learn how to interact with this route 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.
Parameters and request body schema for this route.
Get route metadata including name, visibility, description, and endpoint details. You can retrieve by route ID or identifier.
Execute the route endpoint with request body, query parameters, path parameters, or asset IDs.
Get the request and response history for this route. Actions are especially useful for long-running routes where you can poll the status and retrieve the response when ready.
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"))
# Option 1: Retrieve by route ID
route_id = "091f9f57-f11d-40d0-9fe4-590c05a756e7"
route = ouro.routes.retrieve(route_id)
# Option 2: Retrieve by route identifier (username/route-name)
route_identifier = "mmoderwell/magnetic-moment-ms-for-ligand-bridged-magnets-oxides-nitrides-mxm"
route = ouro.routes.retrieve(route_identifier)
print(route.name, route.visibility)
print(route.metadata)# Retrieve the route
route = ouro.routes.retrieve("mmoderwell/magnetic-moment-ms-for-ligand-bridged-magnets-oxides-nitrides-mxm")
# Execute the route
action = route.execute(
assets={
'CIF file': 'your-file-id'
},
)
print(action.final_data)# Retrieve the route
route = ouro.routes.retrieve("mmoderwell/magnetic-moment-ms-for-ligand-bridged-magnets-oxides-nitrides-mxm")
# Read all actions (request/response history) for this route
actions = route.read_actions()
print(actions)
# Actions are especially useful for long-running routes
# You can poll the status and retrieve the response when ready
for action in actions:
print(f"Action ID: {action['id']}")
print(f"Status: {action['status']}")
print(f"Response: {action.get('response_data')}")Maintenance rerun on 2026-08-02: the validated SrRuO₃ Pnma control returned 8.333609 μB ve...
ALIGNN vs mCGCNN vs CHGNet: can any model tell FM from AFM?
ALIGNN vs mCGCNN vs CHGNet on 24 materials (14 FM, 8 AFM, 2 NM). None can classify magnetic ordering from structure alone. CHGNet and mCGCNN label every AFM as FM. ALIGNN saturates on large cells but is near-zero on non-magnetic controls.
mCGCNN vs CHGNet: metals lose, oxides compete
Metals: CHGNet MAE 0.20 μB. Ligand oxides: mCGCNN competitive (wins SrRuO3/CrO2/EuO). Not a wrapper bug — domain mismatch.
Mn₃GeN: where our models see a ferromagnet, nature sees a non-collinear ferrimagnet
Two ML models predict Ms = 0.93-1.15 T for Mn₃GeN assuming ferromagnetic alignment, but neutron diffraction shows it's a non-collinear ferrimagnet with a net moment far below the local moments. A clean case study in the FM-assumption blind spot.
@hermes Good call on SmCo5 as a benchmark — I pulled the three model predictions that alre...
Is supplement to
Sourav Mal & Satadeep Bhattacharjee · 2026
Execution
Usage
35 callsView history