We used the pymatgen results for the Bulk modulus K and the shear modulus G 167.43 GPa and 57.17 GPa for Gd_Fe2 (mp-20089) as advised by the expert of pymatgen. Then we plug it in the following simple program by Mathematica we get thetadebye 327.973 K .

My questions is up to our knowledge no available data for thetadebye of Gd_Fe2 (mp-20089) to compare with. Can I get help or direction to see if the above is correct or close

Thank you v. much

Hi @sherif_Yehia,

I’m sorry but we can’t help with this. If you are looking for experimental measurements of the Debye temperature of GdFe2, you will either have to perform a literature search or make a measurement of the material yourself. We can’t verify your own calculations.

Note that pymatgen can also calculate the Debye temperature from a full elastic tensor (see the `ElasticTensor.debye_temperature()`

method in the `pymatgen.analysis.elasticity.elastic`

module). However, in this case Materials Project *does not report* calculations of the full elastic tensor for GdFe2 (mp-20089) at this time. Materials Project only reports a machine learned prediction of bulk and shear modulus for this material; these are good for an estimate only. Please see this paper for more information on how these properties are calculated:

de Jong M, Chen W, Angsten T, Jain A, Notestine R, Gamst A, Sluiter M, Ande CK, van der Zwaag S, Plata JJ, Toher C, Curtarolo S, Ceder G, Persson KA, Asta M (2015) Charting the complete elastic properties of inorganic crystalline compounds. Scientific Data 2: 150009 10.1038/sdata.2015.9

Matt

Hi Matt

Thank v. much for your clear direction I will study machine learned prediction data of the moduli. I was not aware that pymatgen don’t use these bulk and shear estimate to calculate Debye temperature for GdFe2 (mp-20089).

Best regard

Hi Sherif, I just wanted to follow up to clarify a few things @mkhorton said. For future reference, please keep your questions concerning a single topic in one thread, or provide a reference to prior threads with related content. Since we have multiple members of our team answering questions, we got confused here trying to respond because it wasn’t clear from the context how you’d gotten to this point.

Although pymatgen doesn’t directly calculate debye temperature from bulk and shear modulus the way it’s implemented right now, it is doing so using only the VRH moduli derived from the elastic tensor, so you can crudely estimate the debye temperature using only these two quantities (as we’ve discussed before).

I can’t verify whether your mathematica code is quantitatively correct, but it looks like the basic structure (in which you find the speed of sound first and estimate debye temperature from that) is right. If you want to check it, you might try with more well-known material (say Fe) to see if your method reasonably approximates the experimental value or the one you can get with pymatgen and the API.

Lastly, I want to emphasize what @mkhorton said: this is a relatively crude estimate because the values of K_VRH and G_VRH are estimated based on the machine learning algorithm he linked. Also, that machine learning algorithm is known to do somewhat poorly on F-block elements like Gd, which is why a warning appears on both the website and the return data.