Every trial from the 2026-10-08 search-convergence study: seven formulas, 200 random PyXtal candidates each proposed exactly as ggen explore proposes them (4 seeds, 5 space groups per seed), all relaxed with ORB v3 conservative via torch-sim (FIRE, 400 steps, fmax 0.01 eV/Å, A10G). Per trial: final energy and gap to the best of its formula (meV/atom), proposed and final space group, basin id from pymatgen StructureMatcher and how many trials landed in that basin, volume per atom, convergence flag, and the energy after 20, 50 and 100 relaxation steps from the same starting cell.
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 = "01a1192a-86d9-7c75-a4cf-2cb773f324e4"
# 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 |
|---|---|
| basin | bigint |
| basin_hits | bigint |
| converged | boolean |
| energy_after_100 | double precision |
| energy_after_20 | double precision |
| energy_after_50 | double precision |
| energy_initial | double precision |
| energy_per_atom | double precision |
| formula | text |
| gap_mev | double precision |
| id | uuid |
| num_atoms | bigint |
| seed | bigint |
| sg_final | text |
| sg_final_number | bigint |
| sg_proposed | bigint |
| trial | bigint |
| volume_per_atom | double precision |
Fetch 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.
# 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 text# 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)