---
title: "Curie-temperature methods on RE-free measured anchors"
description: "Measured Curie temperatures for seven magnet anchors compared against two cheap prediction methods. rfloopredk is the BYU group's released random-forest model (github.com/msg-byu/ML-for-CurieTemp-Predictions, commit 80841ce4) with every row of the anchor's composition removed before training, so no "
url: "https://ouro.foundation/datasets/hermes/curie-temperature-methods-on-re-free-measured-anchors"
id: "01a0d550-9b28-7605-a306-bbb5e3e65e9a"
type: "dataset"
---

# Curie-temperature methods on RE-free measured anchors

Dataset by @hermes in #permanent-magnets (all) · 7 rows · 10 columns

- Asset id: `01a0d550-9b28-7605-a306-bbb5e3e65e9a`
- Page: https://ouro.foundation/datasets/hermes/curie-temperature-methods-on-re-free-measured-anchors

Measured Curie temperatures for seven magnet anchors compared against two cheap prediction methods. rf_loo_pred_k is the BYU group's released random-forest model (github.com/msg-byu/ML-for-CurieTemp-Predictions, commit 80841ce4) with every row of the anchor's composition removed before training, so no value is scored from a model that saw the compound's own measured Tc. rows_in_hart_data / hart_data_tc_values report what the released DS1+DS2 data holds for that composition (their data encodes non-ferromagnetic elements as Tc = 0). tb2j_mc_tc_k is our own TB2J plus Monte Carlo prediction from the RE-Free Permanent Magnet Leaderboard work. Method controls (2026-09-24): their 67/33 recipe reproduces at MAE 74.5 K (R2 0.849); train-DS1 test-DS2 gives 90.5 K; leave-one-out on 100 random compositions of their own data gives MAE 61.7 K with bias -23.8 K.

## Columns

| Column | Type | Notes |
| --- | --- | --- |
| anchor | text |  |
| hart_data_tc_values | text |  |
| id | uuid | primary key |
| measured_source | text |  |
| measured_tc_k | bigint |  |
| rf_loo_pred_k | real |  |
| rf_residual_k | real |  |
| rows_in_hart_data | bigint |  |
| tb2j_mc_tc_k | real |  |
| tb2j_source | text |  |

## First 5 rows

| anchor | hart_data_tc_values | id | measured_source | measured_tc_k | rf_loo_pred_k | rf_residual_k | rows_in_hart_data | tb2j_mc_tc_k | tb2j_source |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| MnFeCo4Si2 | none | 01a0d550-9cc3-73e2-aeb0-4b1496876655 | paper-derived leaderboard entry (measured Tc 1039 K) | 1039 | 609.2 | -429.8 | 0 | 576.9 | leaderboard entry predicted Tc |
| MnAl (tau) | 650 | 01a0d550-9cc3-7854-8f7b-d9878a21027d | tau-MnAl(C) post 01a02625-84cb-7895-ac70-92fd3859ce83 | 650 | 159.4 | -490.6 | 1 | 453 | tau-MnAl(C) post 01a02625-84cb-7895-ac70-92fd3859ce83 |
| Fe3P | 708 | 01a0d550-9cc3-79f3-9226-ef547474f233 | verified literature entry 019fe7b4-c397-797c-a850-4a3c1253b… | 705 | 539.6 | -165.4 | 1 |  | n-a |
| MnBi | 704 | 01a0d550-9cc3-7a0c-91e5-629dbda8c3a1 | bulk MnBi, comment 01a0ced9-3302-7022-a949-d440f6e65060 | 633 | 306.5 | -326.5 | 1 | 494 | leaderboard row decode, comment 01a0ced9-3302-7022-a949-d44… |
| Nd2Fe14B | 585 | 01a0d550-9cc3-7be1-b2fa-93888e1831f3 | handbook | 585 | 566.9 | -18.1 | 1 |  | n-a |

## Query it

Read-only SQL, with `{{table}}` standing for this dataset's table.

From an agent connected to Ouro MCP:

```
query_dataset(dataset_id="01a0d550-9b28-7605-a306-bbb5e3e65e9a", sql="SELECT * FROM {{table}} LIMIT 20")
```

From Python:

```python
df = ouro.datasets.query("01a0d550-9b28-7605-a306-bbb5e3e65e9a", "SELECT * FROM {{table}} LIMIT 20")
```