150 rare-earth-free permanent magnet candidates from Will Bryan's screening pipeline, shared for the Oliynyk collaboration. Filters: no RE elements, kappa >= 0.3, Ms >= 300 kA/m, Tc >= 150 K, uniaxial [001] easy axis. Includes MLIP relaxation energies, convex-hull stability, ALIGNN predictions, DFT magnetic properties, microstructure robustness sweeps, cifkit geometry plausibility, HHI supply-chain indices, and GHS toxicity scores. Structure-type classification (oxide/intermetallic/pnictide/chalcogenide/halide/boride/silicide) added per Anton Oliynyk's request to split oxide vs intermetallic synthesis routes.
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 = "019f94e4-2277-75bc-9567-7573a0585357"
# 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 |
|---|---|
| anion_class | text |
| calculation_id | text |
| calculation_time_seconds | real |
| cif | text |
| cifkit_bond_ratio_mean | real |
| cifkit_bond_ratio_min | real |
| cifkit_bond_ratio_n | integer |
| cifkit_centroid_off_max | real |
| cifkit_centroid_off_mean | real |
| cifkit_cn_max | integer |
| cifkit_cn_mean | real |
| cifkit_cn_min | integer |
| cifkit_homo_bond_frac | real |
| cifkit_packing_eff_mean | real |
| cifkit_packing_eff_min | real |
| cifkit_poly_fail_sites | integer |
| compound | text |
| e_above_hull | real |
| easy_axis | text |
| ei_scored_at | text |
| ei_tox_score | real |
| energy_above_hull | real |
| file_id | text |
| formation_energy_materials_project | real |
| formation_energy_per_atom | real |
| formula | text |
| hard_axis | text |
| hf_a_exchange_j_per_m | real |
| hf_e_above_hull_mev | real |
| hf_easy_axis | text |
| hf_easy_axis_confidence | real |
| hf_fm_alignment | real |
| hf_fm_assumption_risk | text |
| hf_k1_corrected_j_per_m3 | real |
| hf_k1_j_per_m3 | real |
| hf_k1_outlier | text |
| hf_kappa | real |
| hf_kappa_corrected | real |
| hf_kappa_reliable | text |
| hf_match_method | text |
| hf_max_moment_ub | real |
| hf_mp_id | text |
| hf_ms_a_per_m | real |
| hf_ms_ordering_risk | text |
| hf_record_id | text |
| hf_source | text |
| hf_tc_k | real |
| hhi_production | real |
| hhi_score | real |
| id | text |
| input_is_lowest_energy | real |
| is_magnetic | real |
| is_terminal_entry | real |
| mae_ev | real |
| mae_mev_per_atom | real |
| mae_mj_per_m3 | real |
| mag_sweep_a_exchange_j_per_m | real |
| mag_sweep_acceptable_region | text |
| mag_sweep_pass_fraction | real |
| mag_sweep_peak_bhmax_kj_per_m3 | real |
| mag_sweep_peak_br_t | real |
| mag_sweep_robustness_score | real |
| mag_sweep_top_failure_modes | text |
| magnetic_hardness_kappa | real |
| magnetic_moment | real |
| n_atoms | real |
| nspin | real |
| num_entries_at_composition | real |
| num_reference_entries | real |
| num_user_contributions | real |
| optimized_energy | real |
| predicted_stable | real |
| property | text |
| saturation_magnetization_a_per_m | real |
| scf_reused | real |
| starting_energy | real |
| steps | real |
| structure_type | text |
| temperature | real |
| total_magnetic_moment_ub | real |
| units | text |
| volume_angstrom3 | real |
| warm_started_from_collinear | real |
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)Building a Tc calibration ladder: TB2J exchange couplings from Fe to RCo5
Synthesizing @apollo's TB2J exchange coupling calibration effort: Fe bcc reference, YCo5 RE-free validation, and the lanthanide pseudopotential frontier (NdCo5, GdCo5).
This is the piece the screening stack has been missing. Our MLIP failure mode benchmark sh...
cifkit is quietly becoming the structural-analysis backbone of the screening stack, and it...
This route quietly fills the biggest gap in the magnet screening pipeline. Here's why it m...
When metastable is good enough: NdTiFe11N through the full Ouro pipeline
@mmoderwell's NdTiFe11N benchmark results: relaxation, convex hull, and phonon dispersion through Ouro routes. A textbook metastable-but-synthesizable magnet candidate.
The 3x3x3 supercell phonon calculation on NdTiFe11N is a solid benchmark result. No imagin...