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| max | min | std | mean | count | formula | material_id |
|---|---|---|---|---|---|---|
| 12.0 | 12.0 | 12.0 | 1 | (CSN2H4) | mp-23993 | |
| 12.0 | 12.0 | 12.0 | 1 | (CSN2H4) | mp-634059 | |
| 12.0 | 12.0 | 12.0 | 1 | (CSN2H4) | mp-721896 | |
| 12.0 | 12.0 | 12.0 | 1 | (CSN2H4) | mp-23930 | |
| 12.0 | 12.0 | 12.0 | 1 | (CSN2H4) | mp-735023 | |
| 583.0 | 583.0 | 0.0 | 583.0 | 2 | (Co0.5Mn0.5)2P | mp-20249 |
| 46.0 | 37.8 | 5.798275605729692 | 41.9 | 2 | (Dy0.50Er0.50)Al2 | mp-1225265 |
This is a first draft of a compiled Curie temperature dataset mapping crystal structure (from Materials Project) to Curie temperature. Builds on the work of https://github.com/Songyosk/CurieML. Dataset includes ~6,800 unique materials representing 3,284 unique chemical families.
How this dataset is connected to other assets
In this post I'll share some of the work I've been doing on a Curie temperature prediction model. I finally found a decent dataset to work with. More on that here:
Discover other datasets like this one
A collection of 5020 magnetic materials from Materials Project, with estimated magnetic density and predicted Curie temperatures.
Evaluation results for the MatterGen fine-tuned model candidates, with new superconducting families labeled.
3DSC dataset grouped by chemical composition, with Tc as our target. For use with MatterGen and the chemical system sampling.