Hello

I would like to ask regarding the Magnetic moment for GdScSi mp-22480

is this value per formula unit

Final Magnetic Moment

15.811 μB

Thank you in advance

Hello

I would like to ask regarding the Magnetic moment for GdScSi mp-22480

is this value per formula unit

Final Magnetic Moment

15.811 μB

Thank you in advance

Hi @sherif_Yehia,

This value reported is the ‘total magnetization’ of the crystallographic cell as reported by VASP. In the case of GdScSi, it looks like there are two formula units per unit cell, so it would be 7.9 µB per formula unit (this is about right, because the Gd atom is usually of order ~10 µB or slightly smaller, and the Sc, Si atoms are likely non-magnetic, but I would have to check).

We are expanding the information we report for magnetic materials, so please let us know if there is any additional information you would find useful.

Best,

Matt

Hello

Thank you for your quick and clear answer. The nice part is the same result by Wien2k for the total magnetization 15.94507 µB. I would like to see how the DOS and Band structure look like by VASP.

I will send how are they by Wien2k. In addition this ferromagnetic compounds TC about 318 K have magnetocaloric properties according to literature.

One more question I hope to learn more about RFe3 (R=Sm,Gd, ----) I couldn’t found them in materials project

Thanks for your kind help

Thank you

Hello Matt

I would like to resend my request to see the possibility that RFe3 are in materials project calculations agenda or NOT. Those beautiful systems have mganeto Caloric effects (MCE) applications

Thank you very much

Hi Sherif,

If you want to see a certain material on MP, you can use the **crystal toolkit** to submit the structure to our supercomputing infrastructure, either by editing an existing structure or by uploading a CIF file of the material crystal structure. Currently, user crystal structure submissions are only run in the ferromagnetic phase, but those will provide at least an initial of properties that may be useful.

Hi Joseph Montoya

```
Thank you for your advice to get valuable information and I would like to ask
```

kindly if we can use your wonderful system to get Debye temperature for RFe2.compounds those system may have good magneto Caloric effects . For this purpose we calculate the magnetic, electronic and lattice contribution to the total entropy at different magnetic field and temperatures. We use the molecular field to calculate the magnetic part . The coefficient of electronic heat capacity is related to the density of state at Fermi energy. To calculate the lattice contribution one needs to find Debye temperature. Debye Temperature however is related to the speed of sound in crystal.There is a relation between Debye and melting temperature.

This may be also used to estimate Debye temperature If the melting Temperature is known.

To summarize I hope that you can guide me to use your system to get the Debye temperature for RFe2.

Thanks again for your time and effort

Regards

Sherif Yehia

There are a few different ways to estimate Debye temperature. One way is via the elastic tensor:

```
from pymatgen import MPRester
from pymatgen.analysis.elasticity import ElasticTensor
mpr = MPRester() # or with your API key
data = mpr.get_data("mp-13")[0]
structure = mpr.get_structure_by_material_id("mp-13")
et = ElasticTensor.from_voigt(data['elasticity']['elastic_tensor'])
print(et.debye_temperature_from_sound_velocities(structure))
>>> 446.25
```

Note that there are a few different methods in pymatgen to estimate the debye temperature using the elastic tensor. In my experience, `ElasticTensor.debye_temperature_from_sound_velocities`

is the most accurate, but I haven’t done a rigorous check of this to see whether it’s actually the case across all materials.

Also, only around 7000 (of around 70000 total) materials currently have associated elastic tensors. For materials without elastic tensors, you could estimate the debye temperature using the machine learning model for bulk and shear moduli accessible via our API. Here’s a snippet that will fetch the predicted bulk and shear moduli from the aforementioned ML study.

```
from pymatgen import MPRester
from pymatgen.analysis.elasticity import ElasticTensor
mpr = MPRester() # or with your API key
data = mpr.get_data("mp-13", data_type='pred', prop="elastic_moduli")[0]
structure = mpr.get_structure_by_material_id("mp-13")
print(data['elastic_moduli']['K'])
>>> 212.26131768732088
print(data['elastic_moduli']['G'])
>>> 86.83854074462047
```

From these, you can estimate the speed of sound and debye temperature, which you can find the formulation for in most solid-state physics textbooks. The source code of pymatgen’s elasticity module may be helpful in writing the necessary functions of K, G, and the structures.

Hello Joseph_Montoya

Thank you v. much for your clear reply regarding Debye temperature via elastic tensor

we will study python programming language to be able to use that genius method

In addition as the generous offer to allow researcher to use the source code of pymatgen’s elasticity module which will be v. helpful to other

Thanks again for the right direction

Best regards to you and all the team