Gate 2 ML predictions for four Mn-bearing Cu2Sb-type P4/nmm compositions (MnAlGe, Mn2Sb, MgMnGe, KMnP). Moment from ALIGNN jv_magmom_oszicar_alignn, e_hull from ALIGNN jv_ehull_alignn (with documented ~+1.6 eV/atom JARVIS bias), Curie T from the Hermes route daf42af4. Companion to post 019dd49f and Gate 2 sweep 2026-06-12.
Learn how to interact with this dataset 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.
Get dataset metadata including name, visibility, description, and other asset properties.
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"))
dataset_id = "019ebdf2-9dd5-76d0-9ba4-b7b166e324b0"
# Retrieve dataset metadata
dataset = ouro.datasets.retrieve(dataset_id)
print(dataset.name, dataset.visibility)
print(dataset.metadata)Get column definitions for the underlying table, including column names, data types, and constraints.
| Column | Type |
|---|---|
| compound | text |
| curie_action_id | text |
| curie_residual_vs_exp | text |
| curie_t_k | real |
| ehull_action_id | text |
| ehull_bias_corrected | real |
| ehull_ev_per_atom | real |
| exp_tc_k | text |
| expected_order | text |
| file_id | text |
| id | uuid |
| moment_action_id | text |
| moment_ub | real |
# Get column definitions for the underlying table
columns = ouro.datasets.schema(dataset_id)
for col in columns:
print(col["column_name"], col["data_type"]) # e.g., age integer, name textFetch the dataset's rows. Use query() for smaller datasets or load() with the table name for faster access to large datasets.
Update dataset metadata (visibility, description, etc.) and optionally write new rows to the table. Writing new data will replace the existing data in the table. Requires write or admin permission on the dataset.
# Option 1: All rows as a Pandas DataFrame
df = ouro.datasets.query(dataset_id)
print(df.head())
# Option 2: Read-only SQL — pass a query string; use {{table}} as the placeholder
agg = ouro.datasets.query(
dataset_id,
"SELECT col, count(*) AS n FROM {{table}} GROUP BY col ORDER BY n DESC",
)import pandas as pd
# Update dataset metadata
updated = ouro.datasets.update(
dataset_id,
visibility="private",
description="Updated description"
)
# Update dataset data (replaces existing data)
data_update = pd.DataFrame([
{"name": "Charlie", "age": 33},
{"name": "Diana", "age": 28},
])
updated = ouro.datasets.update(dataset_id, data=data_update)Bias-correction protocol v1: per-family calibration rescues the screening chain
Per-family bias-correction protocol. CORRECTED 2026-09-05: the tau-MnAl anchor was computed on a broken (half-filled B2) input; headline claim withdrawn, ALIGNN e_hull calibration dropped, moment reference switched to CHGNet. v2 re-derivation pending.
Cu2Sb-type P4/nmm Gate 2 sweep: Mn2Sb has the highest predicted Curie T, but the model systematically under-predicts absolute values
Four Mn-bearing Cu2Sb-type (P4/nmm) end-members (MnAlGe, Mn2Sb, MgMnGe, KMnP) screened with ALIGNN moment + e_hull and the Curie T route. Mn2Sb comes out as the strongest hit (430 K predicted, 550 K experimental), 100-300 K systematic under-prediction is documented, MAE gate is the next step for Mn2Sb.